A new paradigm is emerging in investment research. While chatbots and copilots have dominated AI conversations for the past two years, 2026 is proving to be the year of agentic AI—systems that don't just respond to queries but autonomously execute complex, multi-step workflows.

For hedge funds and asset managers, this shift represents the most significant evolution in research capabilities since the advent of alternative data.

What Makes AI "Agentic"?

Traditional AI tools are reactive. You ask a question, you get an answer. Agentic AI is fundamentally different: it can reason, plan, and act across multiple steps to accomplish goals.

Consider the difference:

Copilot approach: "Summarize this earnings call transcript."

Agentic approach: "Research competitive dynamics in the EV battery supply chain. Identify key suppliers, analyze their recent earnings commentary, find relevant experts, and prepare a briefing with recommended interview questions."

The agentic system breaks down the complex request, determines the necessary steps, executes them in sequence, and synthesizes the results—all with minimal human intervention.

Why 2026 Is the Inflection Point

The numbers tell the story. According to recent industry surveys, 82% of midsize companies and 95% of PE firms have either begun or plan to implement agentic AI in their operations this year. This isn't speculative interest—it's active deployment.

Several factors are driving this acceleration:

Model Capabilities Have Matured

Large language models have reached a threshold where they can reliably execute multi-step reasoning. The gap between "impressive demo" and "production-ready tool" has finally closed for many use cases.

Infrastructure Is Ready

The supporting infrastructure—vector databases, retrieval systems, orchestration frameworks—has matured. Firms can now build agentic systems without inventing everything from scratch.

Competitive Pressure Is Mounting

With AI-first hedge funds outperforming traditional peers by 3-5% according to recent analyses, the cost of inaction has become clear. Firms that delay adoption risk falling behind permanently.

Agentic AI in Practice: Research Workflows

Where does agentic AI create the most value for investment research teams? Several workflows stand out:

Expert Sourcing and Scheduling

Traditional expert sourcing involves multiple manual steps: defining criteria, searching networks, reviewing profiles, conducting outreach, and coordinating schedules. An agentic system can execute this entire workflow autonomously.

Given a research topic and parameters, it can:

Search across expert networks and professional databases
Evaluate candidates against specified criteria
Generate personalized outreach messages
Coordinate scheduling across time zones
Prepare background materials and suggested questions

What previously required hours of analyst time happens in minutes.

Due Diligence Synthesis

Due diligence on a potential investment involves gathering information from dozens of sources: regulatory filings, news archives, industry reports, prior expert calls, and internal research notes.

Agentic systems excel at this synthesis work. They can autonomously:

Query multiple data sources in parallel
Extract relevant information based on the investment thesis
Identify gaps in available information
Flag potential concerns or areas requiring deeper investigation
Generate structured reports with source citations

Continuous Monitoring

Perhaps most valuable is the shift from reactive to proactive research. Agentic systems can continuously monitor portfolios, alerting teams to:

Unusual language changes in management commentary
Supply chain disruptions affecting portfolio companies
Competitive moves in key markets
Regulatory developments across relevant jurisdictions

This "always-on" research capability was previously impossible without massive analyst teams.

How Leading Firms Are Deploying

The largest players are moving aggressively. BlackRock has introduced Asimov, an agentic AI platform for its fundamental equity business. JPMorgan Chase is investing $18 billion annually in technology with AI as a central focus. Goldman Sachs has committed $6 billion to technology infrastructure.

But the opportunity isn't limited to mega-funds. Mid-sized managers are achieving significant results by:

Starting focused: Rather than attempting enterprise-wide transformation, successful firms identify specific workflows where agentic AI can deliver immediate value.

Building on commercial platforms: Instead of building from scratch, they're leveraging commercial agentic AI tools and customizing for their specific needs.

Maintaining human oversight: The most effective implementations keep humans in the loop for judgment calls while delegating execution to AI agents.

The Architecture of Agentic Systems

Understanding how agentic AI works helps in evaluating solutions. Most systems share common components:

Planning Module

This component breaks complex goals into actionable steps. Given a high-level objective, it determines what needs to happen and in what order.

Tool Access

Agentic systems need to interact with external tools and data sources: databases, APIs, search engines, calendars, and communication systems. The breadth of tool access determines what workflows are possible.

Memory and Context

Unlike simple chatbots, agentic systems maintain context across extended interactions. They remember what they've learned and use it to inform subsequent actions.

Execution Engine

The orchestration layer manages the actual execution of plans, handling errors, adapting to unexpected results, and coordinating parallel operations.

Implementation Considerations

For firms evaluating agentic AI, several factors deserve careful attention:

Data Access and Security

Agentic systems need access to data and tools to be effective. This creates security considerations that must be addressed before deployment. What data can the system access? What actions can it take? How are credentials managed?

Guardrails and Oversight

Autonomous systems need appropriate constraints. What decisions require human approval? How are errors detected and corrected? What audit trails are maintained?

Integration with Existing Workflows

The most successful implementations augment existing workflows rather than replacing them entirely. Analysts should be able to invoke agentic capabilities naturally within their current tools.

Cost Structure

Agentic systems that execute many steps can consume significant computational resources. Understanding the cost model—and implementing appropriate limits—matters for sustainable deployment.

What's Next

The capabilities of agentic AI will continue to expand. Near-term developments to watch:

Multi-modal reasoning: Agents that can process charts, images, and video alongside text, enabling analysis of investor presentations and facility tours.

Cross-system orchestration: Agents that coordinate across multiple specialized systems—CRM, research management, trading platforms—to execute end-to-end workflows.

Collaborative agents: Multiple specialized agents working together, with one handling expert sourcing while another manages document analysis and a third monitors news flow.

Personalized adaptation: Systems that learn individual analyst preferences and research styles, becoming more effective over time.

The Strategic Imperative

The shift to agentic AI isn't optional for serious investment research operations. With the majority of sophisticated firms already implementing these capabilities, those who delay risk permanent competitive disadvantage.

The good news: the technology is mature enough for production deployment, commercial solutions are available, and the implementation playbook is becoming clearer.

The firms that move decisively now will establish advantages in research efficiency, coverage breadth, and insight generation that compound over time.

InsightAgent's AI-powered platform helps investment teams automate expert interview workflows with agentic capabilities. Learn more about our platform.

The Rise of Agentic AI in Investment Research