Agentic AI is reshaping how we think about automation. It provides automated workflows that are no longer about executing predefined steps, but pursuing outcomes. Unlike traditional workflows or standalone large language models that operate one step at a time, Agentic AI can plan, act, and adapt through entire processes, enabling context-aware and goal-driven automation.
What is Agentic AI?
At the heart of Agentic AI is autonomy. AI Agents act as intelligent operators where they can retrieve data, make decisions, evaluate intermediate results, and replan based on new information. Agents don’t require information to be complete or predefined; with access to LLM models, they reason through gaps, make informed decisions, and adjust next steps based on what they learn during execution. While not completely freeform, their decision-making is guided by guardrails and structured access to trusted systems and tools.
These integrated tools include real systems such as APIs, data sources, and workflow logic. Through the coordination of these tools, the agent can take action and deliver valuable output. This coordination between an LLM and workflow tools is made possible by structured frameworks like “Model Context Protocol” (MCP), which expose APIs with defined schemas. These integrations allow the agent to call the right tool at the right moment, package inputs correctly, interpret outputs, enabling intelligent workflows that span across various systems and data types.
Nowhere is this more relevant than in capital markets. With constantly changing regulations, fragmented data, and high-frequency decision cycles, financial institutions face a high level of operational complexity. Where static processes have limitations in dynamic conditions, Agentic AI is uniquely suited to this domain, bringing together intelligent decision-making, flexible tool use, and real-time adaptability to automate workflows in ways that are both powerful and practical.
Agentic AI & Automated Workflows
Agentic AI presents a new approach to automation. Agents dynamically interpret user goals and build workflows instead of relying on predefined flows and logic. They can reason through incomplete data, select tools based on context, and adjust mid-execution. This makes agentic AI highly effective in environments where variability is common. However, agents are not replacements for rules-based automated workflows. They are best suited for cases where reasoning, flexibility, and adaptability are more important than absolute predictability. They require structured access to tools, explicit permissions, and observability to operate safely in production environments.
Conversely, workflow automations are built to deliver consistency. They handle structured, repetitive tasks with speed and precision, such as ingesting files, generating reports, or enforcing compliance checks. Tools like robotic process automation (RPA), scripting, and workflow engines have helped firms process data, enforce rules, and reduce manual effort. These systems are reliable, transparent, and auditable, making them ideal for well-understood processes. They excel when tasks are repeatable, inputs are consistent, and the logic is predefined. However, these workflows are designed for stability and structure, which makes them less effective when the need is more fluid, such as conversational requests, evolving logic, or one-off analytical tasks. In these cases, exception handling and manual reconfiguration can introduce delays and operational friction.
The real power comes from combining the strengths of Agentic AI and Workflow Automation. Agentic AI can sit on top of existing workflows, acting as the intelligence layer that decides which workflows to trigger and when. When the path is clear, the agent can invoke a defined, rules-based automation. When conditions change or context is missing, the agent can reason its way towards a goal-based deliverable. This layered coordination combines the reliability of deterministic systems with the flexibility of intelligent orchestration.
This hybrid model has wide-reaching implications. It reduces the need for workflow re-engineering, allowing firms to respond faster to market changes, and provides automation tools to be more accessible to non-technical teams. By combining stability with adaptability, institutions can scale operations without sacrificing agility. Systems can think when needed and execute with precision.
Figure 1: Key differences between Agentic AI and Workflow Automation
Agentic AI for Capital Markets
Specifically for the capital markets industry, investment firms face a unique combination of complexity, speed, and compliance that puts strain on even well-architected operating models. Funds are juggling fragmented infrastructure across front, middle, and back office using trading platforms, risk engines, custodians, market data feeds, and internal databases, all of which require translation and synchronization. Agentic AI can act as an orchestration layer across these silos, deciding which data to use, what tool to trigger, and when to escalate.
For operations and compliance teams forced to operate reactively, where exceptions, reconciliations, and manual reviews take time and attention away from higher-value work. Agentic AI helps reduce these bottlenecks by surfacing insights proactively and initiating workflows without waiting for human intervention.
Importantly, Agentic AI enhances the technology and data that firms already have, making them smarter rather than replacing them. Combining structured automation with intelligent decision-making empowers lean teams to do more with less and shift their role from task execution to oversight and refinement.
How Everysk’s Automated Workflows Bring Agentic AI to Life
This integration is already in motion at Everysk. Agentic AI is a natural extension of Everysk’s workflow automation framework built from the ground up. To translate these capabilities into production-ready tools, Everysk has built agentic orchestration directly into its workflow engine.
The foundation is Everysk’s powerful library of digital robots designed to perform specific tasks related to capital markets, such as connecting to brokers, performing complex portfolio calculations, generating and distributing dashboards, and much more. Investment firms can design their custom workflows by connecting these modular digital robots on Everysk’s no-code design canvas. These workflows can be combined visually into full-scale operational processes without writing code.
Agentic AI brings another level of customization and ease of use, allowing natural language to be used to achieve complex automation tasks. Agents leverage the knowledge base of an LLM, dynamically decide which tools to use, leverage self-contained automations, pass the appropriate data downstream, and interpret the results. These hybrid workflows can be triggered via natural language prompts or scheduled executions, allowing users to activate complex automations without technical involvement. Static workflows continue to run behind the scenes, while the agent acts as the conductor, stitching them together in real time.
Everysk’s library of more than 100 digital robots can be used to integrate with brokers, call APIs, receive files via sFTP, test compliance rules against portfolios, produce customized reports, distribute information via email, MS Teams, Slack, and many other functionalities. In the MCP framework, AI agents can access these tools to solve daily workflow problems seamlessly.
In addition to this rich set of modular building blocks that allow a non-developer to assemble an automation end-to-end, Everysk also provides a “For Developer” robot and full SDK. The “For Developer” allows our team of engineers and our clients to generate new, custom robots from scratch that leverage Python (or JavaScript) code for their logic, effectively augmenting the library’s limitless possibilities.
To make the deployment of these custom robots even faster, we integrated large language models into the developer workflow. Users describe a function in plain language, and the platform generates contextual code on the spot in order to create custom robots, accelerating the creation of tailored automation logic.
As an example prompt:
“Create a Python function called limit_test that compares two datasets and flags any values that increase more than the thresholds defined in a third dataset called Limits.”
If we provide a workflow with two datasets to compare, combined with a dataset of relevant limits, there is enough context for the LLM to produce quality code to compare and measure the datasets. Having the code generation fully integrated with the automations has enabled Everysk and our clients to prototype new robots in record time and gain unprecedented efficiency and customization. Writing code with the rich context that is provided is an easy task for LLMs.
Figure 2: The “For Developer” robot is created based on the context information it receives from the preceding robots
However, this single-shot approach to code generation for custom robots can be ineffective for more complex prompts and solutions that require sequential reasoning to break the prompt into smaller subtasks. This is best served by Agentic AI, which combines an LLM model’s orchestration and knowledge base with external tools to achieve the desired goal.
Naturally, the next stage of Everysk’s AI evolution was agentic orchestration. Rather than just using LLMs to generate code or answer prompts, we connected them to our automation framework through the MCP. MCP is a standardized way to connect an LLM model to various tools and data sources. The protocol was established by Anthropic and has experienced a strong following. This architecture exposes our digital robots and workflows as callable tools with structured inputs and outputs so that agents can interact with them directly. Based on the user’s goal, the agent decides which tools to call, in what order, and with what data.
Based on a client-server architecture, whereby an MCP client that hosts the LLM (GPT, Claude, or another) connects to various MCP servers, each exposing different tools. Everysk’s agentic AI robots have specialized tools from third parties (scrape a web page, access SEC filings, etc) as well as our own Everysk MCP that exposes various functionalities for controlling our automation elements (datastores, portfolios, files, dashboards).
For example, a prompt like “run a stress test on my SMA portfolio and send me hedge ideas in a PDF” activates a full automation behind the scenes. The agent retrieves the correct portfolio, selects the appropriate stress scenario, interprets the results, generates hedge recommendations, creates the PDF, and delivers it via email, using a mix of Everysk and third-party tools orchestrated through MCP.
Figure 3: Everysk’s Agentic AI uses MCP tools to orchestrate internal workflows and third-party APIs, transforming user prompts into automated deliverables
What makes these agents powerful is the ability to reuse existing automation components while introducing adaptive behavior. Clients don’t need to redesign workflows; they can build tools and expose them to agents that determine when and how to use them. This creates a flexible automation layer with agents acting as intelligent operators in an already scalable system.
With this framework, Everysk’s Agentic AI can perform complex tasks such as:
- Sector Analysis: Find a portfolio’s largest risk contributor by sector, perform deep research on the latest sector developments, generate a PDF report, and distribute by email.
- Fundamental Research: Return the largest holding’s gross margin percentage from the stock’s latest 10-Q filing.
- Portfolio Risk Hedging: Retrieve the latest portfolio, generate an oil stress test, and propose appropriate hedging strategies.
Example: Portfolio Risk Hedging
The following example combines the power of an LLM, a third-party MCP, and Everysk’s own MCP to run entire workflows. This single agentic robot can process a complex request that involves market data, news, SEC filings, and risk calculations from any stock holding in Everysk’s portfolio datastores. The resulting datastore can be incorporated seamlessly into an Everysk workflow.
Prompt:
“Retrieve the portfolio tagged “sma_account” from the workspace “agentic_ai”. Run a stress test using an oil ETF falling 5%. Propose hedging strategies to immunize those losses. Store the hedge ideas in a PDF named ”hedge_ideas.pdf” and send the results to name@everysk.com”
This prompt requires the agent to break down the sequence into subtasks:
Sequence:
| LLM | “I will break this down into steps” | ||
| Everysk MCP | “Let’s get the tagged portfolio from the specified workspace” | get_portfolio_by_tag | portfolio_workspace:”agentic_ai”, portfolio_tag:”sma_account” |
| LLM | < Portfolio Data > | ||
| LLM | “I will use the portfolio’s positions and the USO ETF to stress test a -0.05 magnitude shock” | ||
| Everysk MCP | “Run a portfolio stress test report automation“ | run_portfolio_stress_test | portfolio_id:“port_xyz”,
shock:“USO”, magnitude:“-5%”, email:“name@everysk.com” |
| LLM | “With a portfolio loss of approximately $350, I will now propose some hedges. But first, I need the real-time price for USO” | ||
| 3rd Party MCP | “USO ETF current price” | price_real_time_search | stock: “USO” |
| LLM | < Hedge Ideas > | ||
| Everysk MCP | “I will attach the ideas in the PDF“ | create_file_pdf | name: “hedge_ideas.pdf” |
| LLM | “I’ve completed all steps: 1. Retrieved the portfolio from workspace “agentic_ai” with tag “sma_account” 2. Ran a stress test with: a -5% shock on USO (Oil ETF) 3. Proposed hedge ideas 4. Attached ideas to PDF named “hedge_ideas.pdf” 5. Sent results to name@everysk.com The system has confirmed that the stress test was initiated successfully with an “OK” status. You should receive the results via email at the provided address” |
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This example showcases how powerful the agentic robot can be when provided with MCP tools that are complete automations driven by an API starter. The agent knows it is required to encapsulate the portfolio_id, shock information, and email address to run an Everysk automation that produces a report with the desired calculations and sends it to the recipient. This is an example of how agentic AI is enabled to call specialized, self-contained Everysk automations as seen below:
Figure 4: The Run Portfolio Stress Test Workflow is accessible in the Everysk MCP and called autonomously from the prompt
The Agent calls a workflow that retrieves a portfolio by ID, packages all the information to be able to run a stress test (including the decision by the LLM model to use “USO” as the best ETF to represent an oil shock), and sends a report with the information to an email address, all actions decoded from the natural language prompt.
Figure 5: Everysk workflows are exposed in the Everysk MCP as a tool. The workflow is effectively a custom process API that AI agents can call based on the LLM’s decisions.
By embedding agentic intelligence into the Everysk platform, Everysk enables clients to go beyond static workflows. They can automate with more precision, handle edge cases without manual intervention, and respond faster to changing data and regulation while staying within a controlled, auditable framework.
Conclusion: Smarter Automation Starts With Agentic Intelligence
Automation has long delivered speed, structure, and scale for investment firms. The next leap forward is about executing smarter. Agentic AI enables systems that can interpret goals, adapt to context, and coordinate tools autonomously, unlocking an entirely new layer of operational intelligence.
Everysk is bringing this to life by combining agentic AI with our no-code digital robot platform. We’ve enabled agents to reason through complex tasks, call third-party tools like news scrapers or SEC filing APIs, and run complete automations directly from a single prompt. By integrating with frameworks like MCP, these agents can run workflows on the fly, select the right tools, feed them the correct data, and sequence their actions without needing a hard-coded flow.
Agentic AI offers a practical path forward for firms managing regulatory pressure, complex operations, and fragmented systems. It enhances existing workflows with intelligence, flexibility, and real-time decision support.
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