Contract review agents with human approval gates and immutable audit trails.

Your contract review agent extracts clauses, flags risks, and suggests redlines faster than a junior associate. But your general counsel won't approve it for production use. The issue isn't accuracy — it's accountability. Hatch provides the accountability infrastructure.

The problem.

During an M&A due diligence sprint, your agent processes a data room of 800 contracts over 72 hours. The document parser throws an OOM exception at contract 412. On restart, the agent has no record of which contracts it already processed — it starts from contract 1. Contracts 1 through 411 are processed a second time: duplicate risk flags written to the review database, duplicate summary emails sent to the deal team, duplicate entries in the audit log. The deal team now has two conflicting risk assessments for 411 contracts and no way to know which run's output to trust. The data room review is paused while your engineering team manually reconciles the duplicates under M&A timeline pressure.

The accountability gap that keeps legal agents out of production is specific: when the agent flags clause 14.3 of a vendor agreement as non-standard, the reviewing lawyer needs to see exactly what text the agent read, which policy rule it applied, what the standard position is, what the deviation is, and what the agent recommended. Not a summary — the raw inputs, the rule reference, and the output, all in a queryable record tied to the contract ID and the agent version that processed it. If opposing counsel challenges the review six months after signing, 'the AI flagged it' is not a defensible answer without this trace.

Law firms and legal departments operate under privilege and confidentiality obligations that create a specific infrastructure constraint: contract data cannot transit systems that are not covered by your information security policy. Most SaaS agent platforms process data in shared infrastructure. An agent that sends contract text to a multi-tenant LLM API endpoint, where the request is logged in a vendor's system under opaque retention policies, is a privilege risk your general counsel will not accept. The compute needs to be in your environment, or in a single-tenant environment you control.

What Hatch handles.

Hatch persists a completion record to the WAL after each contract is processed, keyed by document ID and workflow run ID. When the parser crashes at contract 412, the agent resumes from contract 413 on restart. Documents 1 through 411 are marked complete in the WAL; the agent skips them. No duplicate processing, no conflicting risk assessments, no manual reconciliation. The deal team gets one output per contract, from one run.

Every agent action is written to an append-only audit log: the document ID, the page range processed, the clauses extracted with their raw text, the policy rule applied to each clause with its rule ID and version, the risk assessment with confidence score, and the recommended action. The log is written to S3 with Object Lock (WORM) and is queryable by document ID, clause type, risk level, agent version, or review date. Your legal team pulls the full reasoning trace for any flagged clause in under two seconds.

Human approval gates are enforced before any output leaves the agent's workflow boundary. Before a contract summary is sent to the deal team, before a risk flag is escalated to outside counsel, a configured reviewer receives a task in your matter management system with the agent's proposed output and the full reasoning trace. Their approval is logged with their bar ID or user credential and timestamp. The agent's output does not reach any external system until the approval record exists in the WAL.

Hatch deploys entirely within your cloud account or on-premises Kubernetes cluster. Contract text never leaves your environment. LLM inference calls go to a model endpoint you control — Bedrock in your AWS account, Azure OpenAI in your tenant, or a self-hosted model on GPU nodes in your cluster. Hatch does not proxy or log model requests. Your information security team audits the deployment like any other internal service.

Agents that run on Hatch.

Contract reviewer

Ingests contracts from a document queue (S3 event or webhook), extracts text via a PDF parser running in a sidecar container, runs clause extraction and risk assessment against a versioned policy ruleset, writes a structured review record to your matter database with full reasoning, and pauses for lawyer approval before any output is sent to the deal team or external parties.

500+ contracts/week during due diligence periods

Compliance monitor

Subscribes to a regulatory change feed (Thomson Reuters, Wolters Kluwer, or an internal feed), compares each regulatory update against the clause-level index of your active contract portfolio, flags contracts with potentially non-compliant provisions, and generates a structured report with contract ID, clause reference, regulatory citation, and recommended action — queryable by practice area or jurisdiction.

Continuous monitoring across all active contracts

Due diligence agent

Processes data room documents in parallel across a configurable worker pool, extracts key provisions by document type (employment, IP assignment, customer, supplier), identifies liability caps, indemnification obligations, and change-of-control triggers, and produces a structured diligence summary keyed by document ID — with a complete WAL of every document processed so the M&A team knows exactly what was reviewed and when.

1,000+ documents during due diligence sprints

The 2-week PoC.

Take your contract analysis agent. Deploy it on Hatch in your own cloud account. In two weeks, it processes contracts with clause extraction, risk flagging, and lawyer escalation — with an immutable, append-only audit log of every agent decision, queryable by your legal team, and zero data transiting external systems.

500 contracts processed with zero duplicate outputs — idempotency verified by forcing parser failures at document 50, 150, and 300 in the test batch

Full reasoning trace queryable by document ID and clause type — sub-2-second retrieval from append-only audit store

Lawyer approval gate enforced before external transmission — approval logged with user credential and timestamp, verified by audit log inspection

All LLM inference contained within your cloud account — no contract text transits external endpoints, verified by network egress audit

Why now.

The EU AI Act classifies AI systems used for interpreting legal documents in high-risk categories that require conformity assessments, technical documentation, and human oversight mechanisms before deployment. Article 14 specifically requires that high-risk AI systems be designed to allow effective human oversight, including the ability to override outputs. If you deploy a contract review agent without a documented human approval mechanism and an audit trail that satisfies Article 17's record-keeping requirements, you are deploying a non-compliant system into a jurisdiction where enforcement began in 2025. Building the approval and logging infrastructure now is cheaper than a conformity assessment retrofit after the fact.

Have an agent stuck in staging?

Tell us what it does and where it's stuck. We'll scope a 2-week PoC and show you what production looks like.

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