The difference between a coding assistant and an agentic IDE is not just a matter of capability — it’s architectural. A coding assistant responds to prompts. An agentic system operates in a closed loop: it reads the current state of the codebase, plans a sequence of changes, executes them, and verifies the result before reporting completion. That loop is what makes the tooling genuinely useful for non-trivial work.

Agentic CLIs

Most of the conversation around agentic AI focuses on graphical IDEs, but the CLI tools are worth understanding separately. They integrate more naturally into existing scripts and automation pipelines, and in some cases offer capabilities the GUI tools don’t.

The main options currently available:

Claude Code (Anthropic) works with the Claude Sonnet and Opus model families. It handles multi-file reasoning well and tends to produce more explanation alongside its changes, which is useful when the reasoning behind a decision matters as much as the decision itself.

OpenAI Codex CLI is more predictable for tasks requiring strict adherence to a specification — business logic, security-sensitive code, anything where creative interpretation is a liability rather than an asset.

Gemini CLI is notable mainly for its context window, which reaches 1–2 million tokens depending on the model. Large enough to load a substantial codebase without chunking, which changes what kinds of questions are practical to ask.

OpenCode is open-source and accepts third-party API keys, including mixing providers. Relevant for environments with restrictions on approved vendors.

Configuration and Permission Levels

Configuration is stored in hidden directories under the user home folder — ~/.claude/ for Claude Code, ~/.codex/ for Codex. Claude uses JSON; Codex uses TOML. The parameter that actually matters day-to-day is the permission level.

By default, most tools ask for confirmation before destructive operations: file deletion, script execution, anything irreversible. There’s also typically a mode where the agent executes without asking. It’s faster, and it will occasionally remove something that shouldn’t have been removed. The appropriate context for that mode is throwaway branches and isolated environments where the cost of a mistake is low.

Structuring a Development Session

Jumping straight to code generation tends to produce output that looks correct but requires significant rework. The agent didn’t have enough context to make the right decisions, so it made assumptions — and those assumptions have to be found and corrected manually.

Plan Mode

Before any code is written, the agent should decompose the task and surface ambiguities. This is sometimes called Plan Mode or Chain of Thought mode. The output is a list of verifiable subtasks and a set of clarifying questions, typically around:

• Tech stack and framework choices
• Persistence strategy (local storage, SQL, vector database)
• Scope boundaries — what’s in and what’s explicitly out

It feels like overhead. The time is recovered during implementation because the agent isn’t making assumptions that have to be corrected later.

Repository Setup via GitHub CLI

The GitHub CLI (gh) integrates cleanly with agentic workflows. Repository initialization, .gitignore configuration, and GitHub issue creation with acceptance criteria and implementation checklists can all be handled by the agent. Having the backlog populated automatically keeps work visible without manual overhead.

Context Management

The context window is finite. How it’s used determines whether the agent stays coherent across a long session or starts producing inconsistent output. Three mechanisms matter here: rules, skills, and MCP.

Rule Hierarchy

Rules operate at three levels:

User-level rules are global preferences that apply across all projects — language requirements, style constraints, operator restrictions. Set once.

Project rules (.cursorrules or AGENTS.md) are repository-specific: naming conventions, architectural patterns, which shared components to reuse before creating new ones. In a team context, this file deserves the same review process as any other documentation. It tends to get neglected and then blamed when the agent produces inconsistent output.

Conditional rules activate only for specific file patterns. Testing rules that only load when editing .test.ts files, for example. This keeps the context lean when those rules aren’t relevant to the current task.

Skills

Skills are reusable logic packages that the agent loads on demand. Each skill lives in .cursor/skills/ and consists of a skill.md file with frontmatter metadata, plus any executable scripts it needs (Python, Bash, or JavaScript). The agent discovers them semantically or they can be invoked explicitly.

The practical value is context efficiency — instead of re-explaining a pattern every session, the skill carries it and only loads when the task requires it.

Model Context Protocol (MCP)

MCP is the standard for giving agents access to external systems. An MCP server exposes Tools (functions the agent can call) and Resources (data it can query). Configuration is added to the IDE’s config file, after which the agent can interact with connected systems directly.

Common integrations: Slack for notifications, Sentry for querying recent errors related to code being modified, Chrome DevTools for visual validation. The Figma MCP integration is particularly useful — design context can be pulled directly without manual translation of specs into implementation requirements.

Validation

A task isn’t complete until there’s evidence it works. The validation sequence should cover four things:

Compilation and static analysis. The build runs, linters pass. Errors get fixed before the agent reports done.

Test suite. Unit and integration tests for the affected logic must pass. Existing tests must stay green. This sounds obvious and is frequently skipped.

Runtime verification. The agent launches the application in a background process and monitors console output. Runtime errors that don’t surface in tests are common enough that skipping this step is a real risk.

Visual validation. With a browser MCP server, the agent can take a screenshot and compare it against design requirements. Layout and styling issues won’t be caught by any automated test.

Security Configuration

Two files, different purposes, frequently confused:

.cursorignore is a hard block. The agent cannot read files listed here. Use it for .env files, credentials, secrets — anything that shouldn’t leave the local environment. This is the primary security layer.

.cursorindexingignore excludes files from semantic indexing but still allows the agent to read them if explicitly requested. The appropriate use is performance optimization: node_modules, build outputs, generated files that would pollute the index without adding useful signal.

For corporate environments, Privacy Mode should be explicitly verified as enabled rather than assumed. This prevents source code from being stored by the provider or used for model training. Most enterprise tiers include it; the default state varies by tool and version.

Hooks

Hooks are event-driven triggers that run custom scripts at specific points in the agent’s lifecycle. Not necessary for small projects, but worth the setup as the codebase grows.

beforeSubmitPrompt runs before a prompt is sent. Useful for injecting dynamic context — current branch name, recent error logs — or for auditing what’s about to be sent.

afterFileEdit fires immediately after the agent modifies a file. The natural use is triggering auto-formatting or running the test suite, catching regressions as they’re introduced.

pre-compact fires when the context window is about to be trimmed. Allows prioritization of what information should be retained. Relevant for long sessions where important context has accumulated, and the default trimming behavior would discard it.

Parallel Development with Git Worktrees

Sequential work on a single branch is a bottleneck when multiple tasks are running in parallel. Git worktrees allow different branches to exist as separate working directories simultaneously:

git worktree add ../wt-feature-name -b feature/branch-name

Each worktree should have its own .env with unique local ports (PORT=3001, PORT=3002) to prevent dev server collisions. The agent can handle rebases and straightforward merge conflicts autonomously. Complex conflicts still require human judgment — the agent will flag them rather than guess.

The model itself is less of a determining factor than it might seem. Rule configuration, context management, and validation coverage drive the actual quality of the output. A well-configured environment with a mid-tier model will consistently outperform a poorly configured one with a better model. The engineering work shifts toward writing the constraints and verification steps that govern how code gets produced, which is a different skill than writing the code directly, but the productivity difference once it’s in place is significant.

For years, digital wallets in the U.S. have been steady but unspectacular—useful for tap‑to‑pay, not exactly groundbreaking. But the energy in payments today is coming from somewhere unexpected: the crypto wallet world. Stablecoins now exceed $300 billion in circulation, and the infrastructure behind them is delivering the kind of security, interoperability, and user control traditional payments have long needed. 

That shift sets the stage for TRANSACT 2026, where Perficient’s Director of Payments, Amanda Estiverne, will moderate “Keys, Tokens & Trust: How Crypto Wallets Unlock Tomorrow’s Payments,” unpacking how these technologies can finally push digital wallets into their next era. 

“Beyond the session I’m moderating on crypto wallets—and how this technology is set to supercharge tokenization, transform digital identity, and reinvent the very idea of a mobile wallet—I’m fired up for several powerhouse conversations.” – Amanda Estiverne 

Here are three topics we’re looking forward to exploring—and why they matter now. 

1. Security That Actually Builds Trust 

Security remains one of the biggest obstacles to broader U.S. digital wallet adoption—but it’s also the area where crypto wallets offer the clearest blueprint forward. Having spent years securing billions in digital assets in high‑risk environments, crypto wallets have refined capabilities such as multi‑signature authentication, advanced biometrics, tokenization, and decentralized key management. They show how strong security and user‑friendly design can coexist.

“ETA Transact is the gathering place for the entire payments ecosystem. Banks, networks, fintechs, processors, and regulators all come together under one roof to explore what’s next in payments.” – Amanda Estiverne 

2. Interoperability Across Rails and Borders

One of the most persistent challenges in payments is fragmentation—different rails, incompatible systems, and cross‑border friction that create cost and complexity for businesses and consumers alike. Crypto wallets, by contrast, were designed for interoperability from the start. A single wallet can span multiple networks, assets, and payment types without the user having to think about what’s happening behind the scenes.

3. Identity and Personalization in the AI Era

Digital wallets are quickly becoming more than a place to store cards. With AI, they can deliver smarter, more contextual experiences—from personalized rewards to anticipatory recommendations to voice‑enabled commerce. But to power these experiences responsibly, wallets need identity models that balance personalization with user privacy and control.

“Agentic commerce, stablecoins and digital assets, digital identity, personalized payments, and instant payments are among the key themes shaping the conversation. The financial system is undergoing massive transformation, and these emerging areas will play a defining role in the infrastructure of tomorrow’s payments ecosystem.” – Amanda Estiverne 

Discover the Next Payment Innovation Trends 

Transact 2026 is where theory meets practice. Where banks, networks, fintechs, processors, and regulators pressure-test ideas and forge the partnerships that will define the next era of payments.

Amanda’s session focuses on how crypto‑wallet innovations—biometrics, tokenization, decentralized identity, and cross‑border interoperability—can help U.S. mobile wallets finally graduate from tap‑to‑pay conveniences into trusted, intelligent financial hubs.

“It’s where partnerships are forged, new ideas are pressure-tested, and the future of how money moves begins to take shape.” – Amanda Estiverne 

For payment leaders exploring what comes next, this conversation offers a grounded look at the capabilities most likely to redefine digital wallets across security, identity, interoperability, and user experience.

Attending TRANSACT 2026? Come by the Idea Zone at 1:40pm on Thursday, March 19th to hear the exclusive insights. Not attending? Contact Perficient to explore how we help payment and Fintech firms innovate and boost market position with transformative, AI-first digital experiences and efficient operations.

vLLM v0.16.0: Throughput Scheduling and a WebSocket Realtime API

Date: February 24, 2026
Source: vLLM Release Notes

Release Context: This is a version upgrade. vLLM v0.16.0 is the latest release of the popular open-source inference server. The WebSocket Realtime API is a new feature that mirrors the functionality of OpenAI’s Realtime API, providing a self-hosted alternative for developers building voice-enabled applications.

Background on vLLM

vLLM is an open-source library for large language

model (LLM) inference and serving, originally developed in the Sky Computing Lab at UC Berkeley. Over time, it has become the de facto standard for self-hosted, high-throughput LLM inference because of its performance and memory efficiency. Its core innovation is PagedAttention, a memory management technique that lets it serve multiple concurrent requests with far higher throughput than traditional serving methods.

The v0.16.0 release introduces full support for async scheduling with pipeline parallelism, delivering strong improvements in end-to-end throughput and time-per-output-token (TPOT). However, the headline feature is a WebSocket-based vLLM Realtime API for streaming audio interactions, mirroring the OpenAI Realtime API interface and built for voice-enabled agent applications. Additionally, the release includes speculative decoding improvements, structured output enhancements, and multiple serving and RLHF workflow capabilities. Taken together, the combination of structured outputs, streaming, parallelism, and scale in a single release shows continued convergence between “model serving” and “agent runtime” requirements.

Why the vLLM Realtime API Matters for Developers

If you run models on your own infrastructure for cost, privacy, or latency reasons (a trend reinforced by Hugging Face’s acquisition of llama.cpp), this release directly affects your serving stack. The vLLM Realtime API is the standout addition. It gives you a self-hosted alternative to OpenAI’s Realtime API with the same interface, so existing client code can point at a vLLM instance with minimal changes. That alone removes a hard dependency on OpenAI for voice-enabled web applications.

On the throughput side, the async scheduling improvements mean high-concurrency workloads (serving many simultaneous users, for example) will see better performance without needing additional hardware. As a result, more throughput on the same GPUs translates directly to lower cost per request. For workloads where raw token speed matters most, the Mercury 2 diffusion LLM offers a complementary approach that reaches over 1,000 tokens per second.

Date: February 23, 2026
Source: Science

Researchers from MIT and UC San Diego published a paper in Science describing LLM concept vectors and a new algorithm called the Recursive Feature Machine (RFM) that can extract these concept vectors from large language models. Essentially, these are patterns of neural activity corresponding to specific ideas or behaviors. Using fewer than 500 training samples and under a minute of compute on a single A100 GPU, researchers were able to steer models toward or away from specific behaviors, bypass safety features, and transfer concepts across languages.

Furthermore, the technique works across LLMs, vision-language models, and reasoning models.

Why LLM Concept Vectors Matter for Developers

This research points to a future beyond prompt engineering. Instead of coaxing a model into a desired behavior with carefully crafted text, developers will be able to directly manipulate the model’s internal representations of concepts. Consequently, that is a fundamentally different level of control. For context on how quickly the underlying models are evolving, Mercury’s diffusion-based LLM now generates over 1,000 tokens per second, which means techniques like concept vector steering could be applied in near real-time production workloads.

Additionally, it opens the door to more precise model customization and makes it easier to debug why a model behaves a certain way. The ability to extract and transfer concepts across languages is particularly significant for global teams building multilingual applications, since it sidesteps the need to curate separate alignment datasets for each language. For developers interested in building intuition for how models learn representations at a fundamental level, Karpathy’s microGPT project offers a minimal, readable implementation worth studying alongside this research. The practical takeaway is clear: the developers who learn to work with internal model representations, not just prompts, will therefore have a serious edge in building AI-powered applications.

Date: February 23, 2026
Source: Anthropic Blog

Anthropic published a detailed post revealing what it calls an Anthropic distillation attack at industrial scale, accusing three Chinese AI labs (DeepSeek, Moonshot AI/Kimi, and MiniMax) of systematically extracting Claude’s capabilities. According to Anthropic, the labs created over 24,000 fraudulent accounts and generated more than 16 million exchanges with Claude to train and improve their own models.

The post describes the detection methodology, the countermeasures Anthropic has deployed, and the broader policy implications. This comes at a time when DeepSeek is also withholding its latest model from US chipmakers, further deepening the rift between Chinese and Western AI ecosystems. Furthermore, the accusation has generated wide coverage and debate, with some commentators pointing out that the line between “distillation” and “using a competitor’s product for research” is legally and technically contested. This confirms what many in the AI community have long suspected, but the irony is hard to miss: the major AI labs, Anthropic included, have themselves trained their models on vast amounts of copyrighted information from the open web.

Why the Anthropic Distillation Attack Matters for Developers

Here is a threat model most developers have not had to think about before: automated, high-volume extraction of a model’s capabilities through API abuse. If you are building your own models, fine-tuning on outputs from frontier models, or offering AI-powered APIs, this type of distillation attack is now a real intellectual property and security risk you need to account for. API security is becoming a recurring theme across the AI toolchain; for another angle on this, see the recent analysis of MCP protocol security risks and attack surfaces.

On the practical side, expect tighter enforcement from AI providers. Rate limiting, behavioral anomaly detection, and terms-of-service policing are all getting more aggressive. Consequently, if your legitimate workloads involve high-volume API calls or automated pipelines that interact with third-party models, make sure your usage patterns do not look like distillation. Clear documentation, reasonable rate patterns, and proactive communication with your providers will matter more going forward.

I want to play mind games with you. In my last blog post, I shared how to plan an agenda for your brainstorming session. I mentioned that I’m not a big fan of traditional ice breakers – they work fine, but they feel too much like forced socialization rather than a way to prepare your brain for creativity. In this article, I’m going to show you how I loosen teams up and get them thinking with mind games.

Loosen Up by Stretching

The goal is to stretch your imagination. It’s just like stretching before you go for a run (which is also a great thing to do while preparing for brainstorming). We want to disrupt routine thought patterns, and push past the initial “easy” ideas to look for that unique approach and competitive advantage. These mind exercises help people realize that even things that seem impossible can have solutions (even simple solutions). These are NOT a test, it’s OK to not understand, and people should feel welcome to throw out wild or goofy suggestions.

In the rest of this article, I’m going to share several types of mind games: optical illusions, brain teasers, riddles, jokes, and team activities. I’ll share enough that you can run several brainstorming sessions for the same team without reusing them. So pick the ones you like and get to stretching!

Don’t Spoil It!

When you run these in a live brainstorming session, make sure to tell your attendees not to spoil it if they’ve seen one before. Let people have time to think about it and enjoy them. Consider offering to let people leave the room when you reveal the answers.

Optical Illusions

Here are six optical illusions that I love. It shows your attendees that things are not always what they first appear, and that our brains can play tricks on us.

Optical Illusion #1 – Peripheral Drift Rotating Snakes

This is a static image, but as you look around the image it appears to move with rotating circles. (Wikimedia Commons)

Optical Illusion #2 – Double-Image

There is more than one picture in this image. (Wikimedia Commons)

Reveal Answer Can you see the duck? How about the rabbit?

Optical Illusion #3 – Scintillating Grid

Staring at this will cause the white circles to appear like black dots around the edges of your focus. (Wikimedia Commons)

Optical Illusion #4 – Penrose Triangle

This illusion works because a 2D drawing can appear to be 3D but achieve effects that cannot be done in 3D. This shape cannot exist in 3D space. (Wikimedia Commons)

Optical Illusion #5 – Ebbinghause Illusion

Each set of circles has a center circle. Which center circle is the largest? (Wikimedia Commons)

Reveal Answer They are exactly the same size. The sizes of the shapes that surround the center circle changes our perception.

Optical Illusion #6 – Troxler Effect

Stare at the red dot for up to 20 seconds and the blue circle will disappear. (Wikimedia Commons)

Brain Teasers

Next, try these six brain teasers that will stump and entertain your crew. These help teams realize that problems are difficult, but that doesn’t mean they can’t be solved.

Brain Teaser #1 – Cross the Moat

A treasure sits in the middle of a perfectly square island surrounded by a moat 10 feet wide and too deep and treacherous to cross. You need to get across the moat without jumping, climbing, or swimming. There are two sturdy planks 10 feet in length and 3 feet wide. There is nothing to bind the planks together and nothing to cut them with. How can you use the planks to walk safely over the moat?

Reveal Answer Create a “T” shape at the corner of the moat, then go retrieve your treasure!

Brain Teaser #2 – Confusing Math

Can you explain this odd and unexpected problem?

Get a Hint This isn’t math. How can you use the number 2 to end up with a fish? Or the number 3 to arrive at an eight?

Reveal Answer Duplicate the shape of each number, then position, rotate, and/or mirror the shape of the original number to create the word on the right.

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Brain Teaser #3 – Light Switch Problem

You have three incandescent lightbulbs in a small room. Each is controlled by its own light switch outside the room where you cannot see the bulbs or their light. You can flip as many light switches as you want, but you can only check the room once. How do you determine which switch controls each bulb?

Brain Teaser #4 – 9-Dot Puzzle

If you had a print-out of this grid of nine dots, using a pen or pencil, connect all the dots by drawing only four or less straight interconnected line segments without picking the pen up from the paper once you begin. (Wikimedia Commons)

Reveal Answers The solution requires extending your lines outside the grid of nine. Your line segment corners do not have to land on a dot. There are two possible solutions.

Brain Teaser #5 – Birthday Season

Jane was born on Dec. 28th, yet her birthday always falls in the summer. How is this possible?

Brain Teaser #6 – Escape Plan

You are stuck in a concrete room with no windows or doors. The room has only a mirror and a wooden plank for you to use. How do you get out?

Riddles

Here are six riddles to keep their minds moving. Riddles are great because the answer feels like it is within reach, but it is hard to make the connections to come up with the answer – just like real-world problems!

Riddle #1

What occurs once in a minute, twice in a moment, and never in a thousand years?

Riddle #2

What has cities but no houses, forests but no trees, and rivers but no water?

Riddle #3

I am tall when I’m young, and short when I’m old. What am I?

Riddle #4

What is two words but thousands of letters?

Riddle #5

What is the longest word in the dictionary?

Riddle #6

Forward I am heavy. Backward I am not. What am I?

Jokes

Everyone loves a good joke. They are good for brainstorming for two reasons. One, they make you think about what the punchline could be. Two, they get people laughing and comfortable. These are perfect even when you’re not the creative type.

Joke #1

The past, the present, and the future walked into a bar.

Joke #2

What’s the difference between a literalist and a kleptomaniac?

Joke #3

Can February march?

Joke #4

I’d tell you a chemistry joke…

Joke #5

I don’t mind coming to work…

Joke #6

Did you hear about the first restaurant to open on the moon?

Physical Challenges

Some ice breakers are physical challenges, and these are the ones that are an exception of to my rule (of not liking ice breakers). Get people up and moving, blood flowing, minds engaged, and working together to solve a problem!

Challenge #1 – Marshmallow Tower

Each team or person is asked to build a tower as tall as they can using just 20 sticks of dry spaghetti and 20 mini-marshmallows. How tall of a structure can each team get by sticking dry spaghetti into the mini-marshmallows?

This is a trial-and-error activity, those who are not afraid to fail and retry will do the best – children often outperform adults in this exercise. If you have true engineers in the session, they will likely win.

Challenge #2 – The Human Knot

This is a team exercise, so you’ll need 4+ people per team. Each team should stand in a tight shoulder-to-shoulder circle then each member needs to grab hands with two different people in the group. The team must work together to untangle their circle.

Hands must not let go except for a minor change of holding position for comfort. It is not allowed to let go in order to help untangle or to provide additional room. They can step over, under, and through people’s arms. In larger groups it may be possible to untangle into more than one circle.

Challenge #3 – Blindfold Course

Create an obstacle course using chairs, cones, ropes, office supplies…whatever you come up with. Blindfold one team member and have the others guide them through the course using only verbal commands. No touching. No peeking.

Challenge #4 – Toxic Waste Removal

Fill a small bucket with tennis balls (“toxic waste”) and place in the center of a boundary circle of about 10-20 feet in diameter. No one can directly touch the toxic waste or enter the circle. Provide team members with tools such as rope, string, bungee cords, yard sticks, or similar items. The group must find a way to use the tools to get the toxic waste out of the circle and into another small “containment” bucket outside the circle.

Challenge #5 – The Architect

In small groups, one person will be designated the “Architect”, all other group members will be blindfolded. Provide some sort of building materials such as LEGO® bricks, paper cups, straws, tape, or whatever you like. The Architect must verbally instruct the blind “Builders” on how to build something from the materials. This might be a tower judged on height, or a structure judged on creativity. Only the Builders can touch the building materials. If time allows, you can break halfway through, allow the Builders to remove blindfolds and discuss, and then do one last round blindfolded again and guided by the Architect.

Challenge #6 – Paper Airplane Challenge

Start this activity by asking each participant to build a paper airplane on their own. Throw the planes down a hall or in an open area and see whose flies the furthest. Then have small groups build a paper plane together (now that they’ve seen which one flew the best). See which group can win the second round.

Conclusion

I hope you find some of these mind games fun! People who dislike ice breakers will likely find more enjoyment with these mental exercises. But these are more than just fun, these are intentional aids to get people thinking in a new way before you ask them to provide you with industry-changing ideas in a brainstorming session!

……

If you are looking for a partner who will play fun mind games with you, reach out to your Perficient account manager or use our contact form to begin a conversation.

Why Intelligent Insights Matter in Modern Finance

Transforming Data into Actionable Intelligence

Key Features of Oracle IPM Insights

Integrating IPM Insights Across EPM

IPM Insights works natively across Oracle Cloud EPM solutions – Planning, Financial Consolidation and Close, Enterprise Profitability and Cost Management , Tax Reporting, and FreeForm Planning. This integration eliminates silos and ensures consistency across processes. By connecting insights across the full financial lifecycle, organizations can trace the impact of assumptions, drivers, and anomalies from planning through consolidation and final reporting. This unified view reduces reconciliation effort, improves data reliability, and accelerates the close‑to‑forecast cycle.

Best Practices for Optimization

Unlocking the full potential of Oracle IPM Insights requires more than activation – it demands a disciplined approach. Follow these best practices to maximize value:

1. Define Insight Scope Strategically – Configure Insight Definitions for specific data slices aligned with business priorities to keep insights actionable.
2. Incorporate Calendars & Event Context – Annotate insights with business events to distinguish expected fluctuations from true anomalies.
3. Embed Insights into Everyday Workflows – Use Smart View and the Insights dashboard to make insights accessible where planners work.
4. Use Narratives to Strengthen Commentary and Executive Reporting – Incorporate AI‑generated explanations into management decks, close packages, and forecast summaries to improve speed and consistency. This reduces time spent drafting commentary while increasing clarity and precision.
5. Establish Governance & Ongoing Review – Create a monitoring team to fine-tune thresholds, validate models, and drive continuous improvement.

Future Trends in Enterprise Performance Management

1. Driver-Based Forecasting with AutoMLx – Trends are shifting toward intelligent, driver-based forecasting. Oracle EPM leads with Advanced Predictions powered by AutoMLx, enabling multivariate models that incorporate key business drivers for greater accuracy and transparency.
2. Conversational AI Agents for Finance – AI-driven assistants, will allow finance teams to query insights in natural language and receive instant recommendations – making planning more intuitive and collaborative. This shift will not only accelerates decision‑making but will also empower organizations to respond to market changes with greater agility, improving both financial accuracy and overall business performance.
3. Self-Learning Models and Continuous Improvement – Future models will learn from user actions and outcomes, improving accuracy over time. This adaptive capability ensures businesses stay ahead in an ever-changing market.

Why Insights Matter

The ability to detect, predict, and act on insights is no longer optional – it’s a competitive and existential necessity. In an environment where markets shift rapidly, budgets tighten, and expectations for accuracy increase, finance teams must operate with real‑time intelligence rather than backward‑looking reports. Organizations that can rapidly translate data into decisions gain measurable advantages in agility, cost control, and strategic alignment.

Ready to unlock the power of Oracle IPM Insights? Leave a comment or contact us to explore how Oracle EPM Cloud can help you anticipate change, optimize performance, and lead with confidence.

The regulatory landscape for asset managers is shifting beneath our feet. It’s no longer just about filing forms; it’s about data granularity, frequency, and the speed at which you can deliver it. As we move into 2026, the Securities and Exchange Commission (SEC) has made its intentions clear: they want more data, they want it faster, and they want it to be more transparent than ever before.

For financial services executives and compliance professionals, this isn’t just a compliance headache—it’s a data infrastructure challenge. The days of manual spreadsheets and last-minute scrambles are over. The new requirements demand a level of agility and precision that legacy systems simply cannot support. If you’re still relying on manual processes to meet these evolving standards, you’re not just risking non-compliance; you’re risking your firm’s operational resilience.

The Shifting Landscape: More Data, More Often

The theme for 2026 is “more.” More frequent filings, more detailed disclosures, and more scrutiny. The SEC’s push for modernization is driven by a desire to better monitor systemic risk and protect investors, but for asset managers, it translates to a significant operational burden.

Take Form N-PORT, for example. What was once a quarterly obligation with a 60-day lag is transitioning to a monthly filing requirement due within 30 days of month-end. This tripling of filing frequency doesn’t just mean three times the work; it means your data governance and reporting engines must be “always-on,” capable of aggregating and validating portfolio data on a continuous cycle.

The “Big Three” for 2026: Form PF, 13F, and N-PORT

While there are numerous reports to manage, three stand out as critical focus areas for 2026: Form PF, Form 13F, and Form N-PORT. Each has undergone significant changes or is subject to new scrutiny that demands your attention.

Form PF: The Private Fund Data Deep Dive

The amendments to Form PF, adopted in February 2024, represent a sea change for private fund advisers. With a compliance date of October 1, 2026, these changes require more granular reporting on fund structures, exposures, and performance. Large hedge fund advisers must now report within 60 days of quarter-end, and the scope of data required—from detailed asset class breakdowns to counterparty exposures—has expanded significantly. This isn’t just another new report. It’s a comprehensive audit of your fund’s risk profile, delivered quarterly.

Form 13F: The Institutional Standard

For institutional investment managers exercising discretion over $100 million or more in 13(f) securities, Form 13F remains a cornerstone of transparency. Filed quarterly within 45 days of quarter-end, this report now requires the companion filing of Form N-PX to disclose proxy votes on executive compensation. This linkage between holdings and voting records adds a new layer of complexity, requiring firms to seamlessly integrate data from their portfolio management and proxy voting systems.

Form N-PORT: The Monthly Sprint

A shift to monthly N-PORT filings is a game-changer for registered investment companies. The requirement to file within 30 days of month-end means that your month-end close process must be tighter than ever. Any delays in data reconciliation or validation will eat directly into your filing window, leaving little margin for error.

The Operational Burden: Hidden Costs of Manual Processes

It’s easy to underestimate the time and effort required to produce these reports. A “simple” quarterly update can easily consume a week or more of a compliance officer’s time when you factor in data gathering, reconciliation, and review.

For a large hedge fund adviser, we at Perficient have seen a full Form PF filing taking two weeks or more of dedicated effort from multiple teams. When you multiply this across all your reporting obligations, the cost of manual processing becomes staggering. And that’s before you consider the opportunity cost—time your team spends wrangling data is time they aren’t spending on strategic initiatives or risk management.

The Solution: Automation and Cloud Migration

The only viable path forward is automation. To meet the demands of 2026, asset managers must treat regulatory reporting as a data engineering problem, not just a compliance task. This means moving away from siloed spreadsheets and towards a centralized, cloud-native data platform.

By migrating your data infrastructure to the cloud, you gain the scalability and flexibility needed to handle large datasets and complex calculations. Automated data pipelines can ingest, validate, and format your data in real-time, reducing the “production time” from weeks to hours. This isn’t just about efficiency; it’s about accuracy and peace of mind. When your data is governed and your processes are automated, you can file with confidence, knowing that your numbers are right.

Key Regulatory Reports at a Glance

To help you navigate the 2026 reporting calendar, we’ve compiled a summary of the key reports, their purpose, and what it takes to get them across the finish line.

Your Next Move

If your firm would like assistance designing or adopting regulatory reporting processes or migrating your data infrastructure to the cloud with a consulting partner that has deep industry expertise – reach out to us here.

Perficient is proud to announce that we have earned the Databricks Brickbuilder Specialization for Healthcare & Life Sciences, a distinction awarded to select partners who consistently demonstrate excellence in using the Databricks Data Intelligence Platform to solve the industry’s most complex data challenges.

This specialization reflects both our strategic commitment to advancing health innovation through data and AI, and our proven track record of helping clients modernize with speed, responsibility, and measurable outcomes.

Our combined expertise in Healthcare & Life Sciences and the Databricks platform uniquely positions us to help customers achieve meaningful impact, whether improving patient outcomes or accelerating the clinical data review process. This specialization underscores the strength of our capabilities across both the platform and within this highly complex industry. – Nick Passero, Director Data and Analytics

How We Earned the Specialization

Achieving the Databricks Brickbuilder Specialization requires a deep and sustained investment in technical expertise, customer delivery, and industry innovation.

Technical Expertise: Perficient met Databricks’ stringent certification thresholds, ensuring that dozens of our data engineers, architects, and AI practitioners maintain active Pro and Associate certifications across key domains. This level of technical enablement ensures that our teams not only understand the Databricks platform, but can apply it to clinical trials, healthcare claims management, and real world evidence, leading to AI-driven decisioning.

Delivery Excellence: Equally important, we demonstrated consistent success delivering in production healthcare and life sciences use cases. From enhancing omnichannel member services to migrating complex Hadoop workloads to Databricks for a large midwest payer, building a modern lakehouse on Azure for a leading children’s research hospital, and modernizing enterprise data architecture with Lakehouse and DataOps for a national payer, our client work demonstrates both scale and repeatability.

Thought Leadership: Our achievement also reflects ongoing thought leadership, another core requirement of Databricks’ specialization framework. Perficient continues to publish research-driven perspectives (Agentic AI Closed-Loop Systems for N-of-1 Treatment Optimization, and Agentic AI for RealTime Pharmacovigilance) that help executives navigate the evolving interplay of AI, regulatory compliance, clinical innovation, and operational modernization across the industry.

Why This Matter to You

Healthcare and life sciences organizations face unprecedented complexity as they seek to unify and activate data from sensitive datasets (EMR/EHR, imaging, genomics, clinical trial data). Leaders must make decisions that balance innovation with security, scale with precision, and AI-driven speed with regulatory responsibility.

The Databricks specialization matters because it signals that Perficient has both the technical foundation and the industry expertise to guide organizations through this transformation. Whether the goal is to accelerate drug discovery, reduce clinical trial timelines, personalize therapeutic interventions, or surface real-time operational insights, Databricks provides the engine and Perficient provides the strategy, implementation, and healthcare context needed to turn potential into outcomes.

A Thank You to Our Team

This accomplishment is the result of extraordinary commitment across Perficient’s Databricks team. Each certification earned, each solution architected, and each successful client outcome reflects the passion and expertise of people who believe deeply in improving healthcare through better data.

We’re excited to continue shaping the future of healthcare and life sciences with Databricks as a strategic partner.

To learn more about our Databricks practice and how we support healthcare and life sciences organizations, visit our partner page.

Editor’s Note: We are thrilled to feature this guest post by Tracy Julian, Financial Services Industry Lead & Architect at Perficient. With over 20 years of experience across retail banking, wealth management, and fintech, Tracy is a systems architect who specializes in turning complex data hurdles into high-velocity, future-ready AI solutions.

Executive Summary 

Financial services organizations face mounting pressure to deliver highly personalized client experiences while navigating increasingly complex regulatory requirements. At the same time, relationship managers and advisors spend a significant portion of their week searching for client information across disconnected systems. This administrative burden reduces time available for strategic client engagement and limits the ability to proactively identify cross-sell, retention, and risk management opportunities. 

Agentforce, Salesforce’s enterprise-grade agentic AI platform, addresses these challenges head-on. By automating data aggregation, surfacing real-time insights, and embedding compliance-aware intelligence directly into workflows, Agentforce helps financial services teams operate more efficiently and intelligently. 

This article explores real-world Agentforce financial services use cases and provides a practical implementation roadmap for organizations evaluating AI agent deployment. 

Key Takeaways 

• Agentforce reduces client research time through automated, multi-source data aggregation 

• Four proven Agentforce financial services use cases across banking, wealth, and asset management 

• A 4–6 week implementation timeline is achievable with proper planning 

• Built-in compliance automation aligned with SOC 2 and financial services standards 

The Challenge: Data Fragmentation in Modern Financial Services 

Financial services teams across B2B banking, wealth management, registered investment advisors (RIAs), and workplace services face a shared set of challenges that directly impact revenue, efficiency, and client satisfaction. 

1. Information Silos Create Operational Inefficiency

• Client data is scattered across multiple Salesforce orgs, legacy core banking systems, portfolio management platforms, and document repositories 

• Financial advisors manage information across many different systems 

• There is no single, unified view of client relationships, risk indicators, or cross-sell opportunities 

2. Time-Intensive Meeting Preparation

• Client-facing teams spend disproportionate time on administrative tasks rather than strategic interactions 

• Relationship managers manually compile company summaries, account histories, and risk assessments before each meeting 

• Information retrieval delays slow response times to client inquiries 

3. Escalating Regulatory Complexity

• Increasing regulations around data privacy (GDPR, CCPA, GLBA), personally identifiable information (PII), and record retention 

• Manual compliance reviews create operational bottlenecks and increase the risk of human error 

• Document scanning for sensitive data (SSNs, account numbers, tax IDs) is often reactive rather than preventive 

4. Missed Revenue Opportunities

• Without unified intelligence, leaders struggle to identify upsell, cross-sell, and retention risks in real time 

• Fragmented data limits proactive account planning and relationship management 

• Inconsistent visibility into consultant and intermediary relationships reduces partner channel effectiveness 

Real-World Example: Multi-Org Complexity 

A Perficient financial services client operates 20+ production Salesforce orgs across marketing, sales, and service. This complexity has resulted in: 

• Significant manual effort by relationship managers searching for client information 

• Inconsistent data interpretation across sales and service teams 

• Compliance vulnerabilities caused by manual PII identification processes 

• Delayed opportunity identification due to siloed account intelligence 

This scenario is common across enterprise financial services organizations—and represents one of the most compelling Agentforce financial services use cases. 

How Salesforce Agentforce Helps 

Agentforce is Salesforce’s next-generation AI platform, combining: 

• Natural language processing (NLP) for conversational interfaces 

• Multi-source data aggregation across Salesforce objects, external systems, and documents 

• Workflow automation triggered by agent-driven insights and actions 

• Compliance-aware processing with PII detection and security controls 

• Real-time intelligence generated from both structured and unstructured data 

Unlike traditional chatbots or rule-based automation, Agentforce agents: 

• Understand context and intent from natural language queries 

• Access and synthesize information from multiple data sources simultaneously 

• Generate actionable insights and recommendations—not just raw data 

• Learn from user interactions to improve relevance over time 

• Integrate seamlessly with existing Salesforce workflows and third-party systems 

Agentforce leverages Salesforce Einstein AI, Data 360 for unified data access, and the Hyperforce infrastructure to deliver enterprise-grade security, compliance, and trust for financial services use cases. 

Four High-Impact Agentforce Financial Services Use Cases 

The following Agentforce use cases have been developed specifically for financial services and can typically be implemented within four weeks. 

Client Intelligence Agent: Gain 360-Degree Relationship Insights 

The Client Summary Agent consolidates comprehensive client intelligence in seconds, eliminating manual data gathering. It aggregates: 

• Company & Contact Details: Legal entity structure, key decision-makers, organizational hierarchy 

• Financial Position: Account balances, asset allocation, liabilities, portfolio performance 

• Relationship Health: Engagement scores, activity frequency, NPS data, retention risk indicators 

• Opportunity Pipeline: Active deals, proposal status, estimated close dates, win probability 

• Service History: Open and closed cases, resolution times, satisfaction ratings 

• Interaction Timeline: Meetings, calls, emails, and all historical touchpoints 

Business Outcome
Relationship managers can prepare for meetings faster, personalize conversations, and proactively identify engagement and retention risks. Time previously spent gathering data is redirected to strategic client interactions. This represents one of the foundational Agentforce financial services use cases. 

Account Relationship Agent: Manage Complex Accounts & Client Risk 

For firms that work with consultants, brokers, or intermediaries, the Account Relationship Agent provides a unified view of partner relationships by consolidating: 

• Partner Profile: Firm details, key contacts, AUM/AUA influenced, areas of specialization 

• Referral History: Opportunities sourced, conversion rates, deal size, revenue attribution 

• Engagement Metrics: Meeting cadence, co-marketing activity, webinar participation, content engagement 

• Pipeline Analysis: Active referrals by stage, forecasted revenue, deal aging 

• Collaboration Activity: Shared plans, joint calls, tasks, and communication history 

Business Outcome
Sales teams gain clarity into partner performance and potential, enabling better territory planning, stronger collaboration, and more strategic channel investment. 

Client Prospect Agent: Optimize Sales Intelligence & Next Best Action 

The Client Prospect Agent transforms raw data into actionable sales intelligence by analyzing: 

• Company Intelligence: Industry position, competitive landscape, growth signals, news mentions 

• Buying Signals: Website engagement, content consumption, event attendance, RFP activity 

• Relationship Mapping: Existing connections, decision-makers, organizational structure 

• Whitespace Analysis: Current services versus product catalog, cross-sell and upsell opportunities 

• Next Best Actions: Prioritized recommendations based on engagement and firmographic data 

Business Outcome
Sales teams can prioritize accounts more effectively, uncover whitespace opportunities, and focus on actions that accelerate deal progression. This Agentforce financial services use case is most beneficial for acquisition teams. 

Document Scanning Agent: Automate PII Compliance Safeguards 

Regulatory compliance is non-negotiable in financial services. The Document Scanning Agent provides automated, pre-upload document scanning for: 

• Social Security Numbers (SSNs): Multiple formats (XXX-XX-XXXX, XXXXXXXXX) 

• Tax Identification Numbers (TINs/EINs): Business and individual identifiers 

• Account Numbers: Bank, credit card, and brokerage accounts 

• Passport Numbers: Government-issued identification 

• Custom PII Patterns: Configurable regex for institution-specific data types 

Business Outcome
Organizations reduce human error, strengthen compliance posture, and protect sensitive client data—automatically and proactively. 

Getting Started: Next Steps for Your Organization 

If your organization is evaluating Agentforce, consider the following steps: 

1. Assess Your Current State

• Map data fragmentation across systems and or within the Salesforce org across objects  

• Quantify time spent on manual data gathering 

• Identify high-impact pain points 

• Establish baseline metrics for measuring improvement 

2. Define Success Criteria

• Business outcomes: Efficiency gains, revenue impact, compliance risk reduction 

• Adoption targets: Percentage of users actively engaging with agents 

• Technical performance: Accuracy, response time, data completeness 

• ROI expectations: Payback period and time to value 

3. Prioritize Use Cases

• Identify quick-win Agentforce financial services use cases that deliver value in 30–60 days 

• Assess team readiness and change appetite 

• Evaluate data availability and quality 

• Align use cases to regulatory risk and compliance priorities 

4. Engage Expert Partners

• Schedule a discovery workshop with Perficient 

• Review reference architectures and live demonstrations 

• Develop a phased implementation roadmap 

• Establish governance, KPIs, and success metrics 

AI Agents as a Competitive Advantage in Financial Services 

The financial services industry is at an inflection point. Organizations that successfully deploy Agentforce financial services use cases to augment human expertise will gain durable competitive advantages, including: 

• Superior client experiences through faster, more personalized, and proactive service 

• Improved operational efficiency by shifting effort from administration to relationship management 

• Revenue growth through earlier identification of cross-sell, upsell, and retention opportunities 

• Increased compliance confidence with automated safeguards that reduce regulatory risk 

• Data-driven decision-making powered by unified, real-time intelligence 

Agentforce represents Salesforce’s most significant AI advancement for financial services—combining trusted CRM data with cutting-edge agentic AI capabilities. Organizations that move quickly, but strategically, will establish lasting advantages in client relationships, operational efficiency, and market leadership. 

Meet Your Expert 

Tracy Julian
Financial Services Industry Lead & Architect, Salesforce Practice 

Tracy brings more than 20 years of financial services experience in retail banking, wealth management, capital markets, and fintech, spanning both industry and consulting roles with firms including the Big 4 across the U.S. and EMEA. 

She leads Perficient’s financial services industry efforts within the Salesforce practice, partnering with clients to define the vision and goals behind their transformation. She then uses that foundation to build smarter, future-ready solutions that deliver business first, scalable solutions across strategy, cloud migration, and innovation in marketing, sales, and service. 

A systems architect by trade, Tracy is known for aligning teams around a shared vision and solving complex problems with measurable impact. 

Hello Trailblazers!

Take a scenario where you are creating a record in Salesforce, and you are not getting any kind of confirmation via notification whether your record is created successfully or it throws any Alert or Warning. So, for this, Salesforce has functionality called “Toast Notifications”.

Toast notifications are an effective way to provide users with feedback about their actions in Salesforce Lightning Web Components (LWC). They appear as pop-up messages at the top of the screen and automatically fade away after a few seconds.

So in this blog post, we are going to learn everything about Toast Notifications and their types in Salesforce Lightning Web Components (LWC), along with the real-world examples.

So, let’s get started…

In Lightning Web Components (LWC), you can display Toast Notifications using the Lightning Platform’s ShowToastEvent. Salesforce provides four types of toast notifications:

1. Success – Indicates that the operation was successful.
   • Example: “Record has been saved successfully.”
2. Error – Indicates that something went wrong.
   • Example: “An error occurred while saving the record.”
3. Warning – Warns the user about a potential issue.
   • Example: “You have unsaved changes.”
4. Info – Provides informational messages to the user.
   • Example: “Your session will expire soon.”

Example Code for a Toast Notification in LWC:

import { ShowToastEvent } from 'lightning/platformShowToastEvent';

const event = new ShowToastEvent({
    title: 'Success!',
    message: 'Record has been created successfully.',
    variant: 'success' // Can be 'success', 'error', 'warning', or 'info'
});
this.dispatchEvent(event);

So, here is an example of the Toast Notification.

So this way, you can write toast notification code and make changes according to your requirements.

In the next part of this blog series, we will explore what a success toast notification is and demonstrate how to implement it through a practical, real-world example.

Until then, Keep Reading !!

Related Posts:

1. Toast Notification in Salesforce
2. Toast Event: Lightning Design System (LDS)

You Can Also Read:

1. Introduction to the Salesforce Queues – Part 1
2. Mastering Salesforce Queues: A Step-by-Step Guide – Part 2
3. How to Assign Records to Salesforce Queue: A Complete Guide
4. An Introduction to Salesforce CPQ
5. Revolutionizing Customer Engagement: The Salesforce Einstein Chatbot

Machine Learning (ML) may sound technical; however, once you break it down, it’s simply about teaching computers to learn from data—just like humans learn from experience.

What Is Machine Learning?

Machine Learning is a branch of artificial intelligence; in essence, it allows models to learn from data and make predictions or decisions without the need for explicit programming.

Every ML system involves two things:

• Input (Features)
• Output (Label)

With the right data and algorithms, ML systems can recognize patterns, make predictions, and automate tasks.

Types of Machine Learning

1.1 Supervised Learning

Supervised learning uses labeled data, meaning the correct answers are already known.

Definition

Training a model using data that already contains the correct output.

Examples

• Email spam detection
• Predicting house prices

Key Point

The model learns the mapping from input → output.

1.2 Unsupervised Learning

Unsupervised learning works with unlabeled data. No answers are provided—the model must find patterns by itself.

Definition

The model discovers hidden patterns or groups in the data.

Examples

• Customer segmentation
• Market basket analysis (bread buyers also buy butter)

Key Point

No predefined labels. The focus is on understanding data structure.

1.3 Reinforcement Learning

This type of learning works like training a pet—reward for good behavior, penalty for wrong actions.

Definition

The model learns by interacting with its environment and receiving rewards or penalties.

Examples

• Self-driving cars
• Game‑playing AI (Chess, Go)

Key Point

Learning happens through trial and error over time.

2. Core ML Concepts

2.1 Features

Input variables used to predict the outcome.

Examples:

• Age, income
• Pixel values in an image

2.2 Labels

The output or target value.

Examples:

• “Spam” or “Not Spam”
• Apple in an image

2.3 Datasets

When training a model, data is usually split into:

• Training Dataset
  Used to teach the model (e.g., 50% of data)
• Testing Dataset
  Used to check performance (the remaining 50%)
• Validation Dataset
  Fresh unseen data for final evaluation

2.4 Overfitting & Underfitting

Overfitting

The model learns the training data too well—even the noise.
Good performance on training data
✘ Poor performance on new data

Underfitting

The model fails to learn patterns.
Fast learning
✘ Poor accuracy on both training and new data

3. Common Machine Learning Algorithms

Below is a simple overview:

3.1 Regression

Used when predicting numerical values.

Examples

• Predicting sea level in meters
• Forecasting number of gift cards to be sold next month

Not an example:
Finding an apple in an image → That’s classification, not regression.

3.2 Classification

Used when predicting categories or labels.

Examples

• Identifying an apple in an image
• Predicting whether a loan will be repaid

3.3 Clustering

Used to group data based on similarity.
No labels are provided.

Examples

• Grouping customers by buying behavior
• Grouping news articles by topic

4. Model Evaluation Metrics

To measure the model’s performance, we use:

Basic Terms

• True Positive
• False Negative
• True Negative
• False Positive

Important Metrics

• Accuracy – How often the model is correct
• Precision – Of the predicted positives, how many were correct?
• Recall – How many actual positives were identified correctly?

These metrics ensure that the model is trustworthy and reliable.

Conclusion:

Machine learning may seem complex; however, once you understand the core concepts—features, labels, datasets, and algorithms—it quickly becomes a powerful tool for solving real‑world problems. Furthermore, whether you are predicting prices, classifying emails, grouping customers, or training self‑driving cars, ML is consistently present in the technology we use every day.

With foundational knowledge and clear understanding, anyone can begin their ML journey.

Additional Reading

• Understanding Common AI Workloads – Explained Simply / Blogs / Perficient
• What is Artificial Intelligence (AI)? / Blogs / Perficient
• What is Microsoft Copilot Studio? / Blogs / Perficient