Engineering Autonomous Agents for Due Diligence

The marketing literature is full of "autonomous AI agents" that promise to revolutionize due diligence workflows. Most of them are glorified chatbots with API access. True autonomous agents—systems capable of decomposing complex financial analysis tasks, executing multi-step workflows, and self-correcting when they encounter ambiguity—require rigorous engineering. This is systems architecture, not prompt engineering. Here's what separates production-grade autonomous agents from demos.

The Architecture Challenge: Beyond Single-Model Inference

Building an autonomous agent for due diligence isn't a question of finding the right foundation model. It's a question of orchestration. A single LLM call can extract data from a CIM. But analyzing a 200-page confidential information memorandum, cross-referencing it with industry benchmarks, flagging EBITDA adjustment opportunities, and producing an investment committee memo requires coordination across multiple specialized sub-agents.

This creates three core engineering problems: state management (how do agents share context without memory leaks?), tool orchestration (how do you reliably chain function calls without cascading failures?), and error handling (what happens when an agent encounters a malformed Excel file or ambiguous language in a legal clause?).

Three Critical Design Patterns

1. Task Decomposition with Specialized Agents. Instead of a monolithic "due diligence agent," our Pre-Screening Agent uses a modular architecture: a financial analysis agent (trained on EBITDA normalization), a legal review agent (flagging regulatory risks), and an ESG compliance agent (screening against impact criteria). Each agent has a narrow scope and explicit success criteria. When the orchestrator receives a new deal, it routes subtasks to the appropriate specialist.

2. Human-in-the-Loop Verification with Confidence Thresholds. Autonomous doesn't mean unsupervised. Every agent output includes a confidence score. When confidence drops below a defined threshold—say, 85% certainty on an EBITDA adjustment—the system escalates to human review rather than proceeding with a guess. This prevents the "95% accuracy problem": in high-stakes finance, 5% error rates can cost millions.

3. Iterative Refinement Loops. Production agents need self-correction mechanisms. If the financial analysis agent encounters missing data in a revenue table, it doesn't fail silently. It generates a clarification request, logs the gap, and continues processing other sections. This allows analysts to provide missing inputs without re-running the entire workflow.

Real-World Constraints: Security, Cost, Latency

Data Security. Financial due diligence involves highly confidential data. Our zero-retention architecture ensures data exists only during active processing—never as training material for public models.

Cost Management. Multi-agent systems multiply inference costs. A naive implementation might make 50+ LLM calls per deal. We use caching, selective tool invocation, and smaller models for low-complexity subtasks to keep costs predictable.

Latency Requirements. A screening memo that takes 8 hours to generate isn't autonomous—it's a batch job. Real-time orchestration with parallel agent execution keeps our average processing time under 4 hours.

Engineering autonomous agents for due diligence is closer to distributed systems design than to prompt engineering. Task decomposition, verification checkpoints, and iterative refinement aren't optional features—they're architectural requirements. The firms that understand this will deploy agents that scale. Those that don't will deploy demos.