The Road to Agentic AI in Finance: Turning Tacit Knowledge Into Actionable Intelligence

The Road to Agentic AI in Finance: Turning Tacit Knowledge Into Actionable Intelligence

For all the noise around AI in finance, most of what exists today is still “ask and answer” tooling – chat interfaces dressed up as innovation. Users can type a question and receive a passable summary. Helpful? Sometimes. Transformative? Hardly.

The real holy grail is agentic AI – systems that can autonomously monitor processes, reason over context, break down complex tasks, and surface changes without being prompted. But as teams try to push beyond summaries and into real decision support, they quickly run into the same wall: AI can only act as intelligently as the organization can describe how the job should be done.

This is the throughline of every successful agentic project we’ve seen: before you build the agent, you must articulate the assignment. Firms must externalize the knowledge their internal users currently carry in their heads. That includes the heuristics, unwritten rules, sequencing of steps, and tacit signals that make an expert an expert.

When you strip away the hype, an agent is nothing more than a machine executing codified expertise – continuously, consistently, and at scale. Once that foundation is in place, true autonomy becomes possible.

Defining Agentic Behavior

When you think about what makes agentic systems useful, the need for codified expertise becomes obvious. The real value lies in letting a system watch, interpret, and prioritize the way an experienced and responsible analyst would – not randomly, not generically, but with a sense of what matters and when.

• Monitoring long-running processes – An agent can keep constant watch over corporate actions, deal pipelines, and rulemaking processes – but only if it knows what events matter, why they matter, and what should happen next. Those rules don’t come from an LLM; they come from documenting the tacit logic human analysts use.
  
  
• Breaking tasks into components – Firms are beginning to deploy multi-agent pipelines: one sub-agent constructs a deal summary, another interprets methodology, a third analyzes liquidity. These steps only function because the underlying roles have been broken into discrete, documented reasoning units.
  
  
• Managing state and context over time – Agents must know the past (“What happened yesterday?”), the present (“What’s the latest movement?”), and the implied next step (“What should I check now?”). This sequencing mirrors the mental model of a human expert. That model must be captured and externalized, not left implicit.
  
  
• Adaptive output – When an advisor investigates a deal, an experienced analyst knows where to start: liquidity if it’s highly traded, antitrust if it’s concentrated, sentiment if the narrative moved. An agent can only adapt the workspace in this way if firms translate expert intuition into structured cues and triggers.

In all of these instances, agentic behavior is not simply a capability, but an expression of documented expertise in dynamic form. Viewed through this lens, the limitations of mere chat use cases become clear. AI can’t meaningfully contribute to higher-value financial work until the work itself is made explicit.

Why not? First, chat breaks down where tacit knowledge begins. The more complex an ask, the more likely it is to overgeneralize, miss context, and collapse nuance. If agents are left unstructured, users quickly become lost in the prompt engineering wilderness.

This helps explain why reducing cognitive load requires encoded reasoning. The reason analysts and PMs spend hours gathering and synthesizing information is not lack of data, nor a want of analytical capabilities – it’s that much of the synthesis is happening in their heads. Only once that intellectual scaffolding is written down can an agent preload the right context, prepare the right view, and take over the rote orchestration. This is true even on a single-user scale, but it becomes even more essential at the organizational level. Codifying decision-making logic protects the firm from the knowledge gaps, turnover, and interpretive drift that arise when working across regions and teams.

Finally, there’s the practical reality that autonomous systems cannot be allowed to improvise. Agentic workflows are only safe to adopt when decision-making is grounded in deterministic logic and captured expertise, not generative speculation. Ignoring the need for these guardrails is an easy way to clutter the experience, erode user trust, and land squarely in the regulatory crosshairs.

Requirements for Designing Agentic Systems

Every design requirement for agentic AI traces back to a single prerequisite: the agent must inherit clearly documented reasoning patterns. That means the organization itself must commit to surfacing, structuring, and aligning on that knowledge.

• Codified Expert Logic
  
  • Design requirement: Agents need explicit rules, heuristics, and sequences of reasoning to operate safely.
  • Organizational support: Document tacit knowledge – the unwritten steps, decision triggers, and interpretive cues experts use.
    
    
• Clear Autonomy Boundaries
  
  • Design requirement: Define what the agent can initiate, what requires confirmation, and what is out of scope.
  • Organizational support: Establish shared decision criteria and escalation rules across desks and teams.
    
    
• Auditability and Explainability
  
  • Design requirement: Agents must show provenance, cite sources, and justify outputs.
  • Organizational support: Align teams on the firm’s canonical methodologies so reasoning can be expressed consistently.
    
    
• Stable Handling of Long-Running Processes
  
  • Design requirement: Agents need structured workflows to monitor, pause, resume, and evaluate multi-step tasks.
  • Organizational support: Map end-to-end processes clearly; eliminate variations in how different teams handle stages and handoffs.
    
    
• Institutional Knowledge, Not Tribal Knowledge
  
  • Design requirement: Agents need consistent logic across use cases to avoid fragmentation.
  • Organizational support: Convert individual mental models into shared, durable organizational logic that scales across teams, roles, and applications.

Conclusion: When Autonomy Becomes the Baseline

Agentic AI shifts the center of gravity from interfaces and answers to workflows. The value isn’t in simply conveying more information, but in shaping the work itself – watching, interpreting, and preparing the steps an expert would take. Doing this well demands strong foundations and clearer reasoning than most firms have ever had to articulate.

But firms that take that step now will find themselves with something rare: systems that don’t just respond to their users, but work alongside them. And soon, that will be the expectation rather than the exception.

If your organization is exploring agentic AI or optimizing its UX more broadly, we’d love to discuss. Get in touch with us.