My AI Agent Was Escalating Every Contract. One Decision Layer Fixed It 📑🤖📑🤖

A developer built an Enterprise Contract Intelligence Agent using Hermes Agent, but the first version failed by escalating nearly every contract due to a lack of a confidence-based decision layer. By redesigning the workflow to include risk scoring and confidence thresholds, the agent now intelligently decides whether to approve low-risk contracts or escalate high-risk ones to legal review. The system dynamically plans tasks, extracts clauses, and evaluates risk, behaving like a real enterprise analyst rather than a rule-based script.

This is a submission for the Hermes Agent Challenge: Build With Hermes Agent While experimenting with enterprise AI agents, I noticed a common problem: Contract reviews are painfully manual. Vendor agreements, NDAs, MSAs, and SOWs often require legal teams to manually inspect: I wanted to see: Can an AI agent intelligently decide what to review and when to escalate? So I built an Enterprise Contract Intelligence Agent powered by Hermes Agent . Instead of simply extracting text from contracts, the agent plans tasks, invokes tools, reasons through risks, and decides whether a contract actually requires legal review. The interesting part? My first version failed badly. Hermes Agent was escalating almost every contract. NDAs. Vendor agreements. Even low-risk contracts. Technically the system worked. Practically? Completely unusable. The issue turned out to be simple: The agent lacked a confidence-based decision layer. If a single clause looked risky, Hermes escalated immediately. That created too many false positives. So I redesigned the workflow. Now Hermes Agent: The result: Hermes now behaves much more like a real enterprise analyst instead of a rule-based script. Example output: Contract Type: Vendor Agreement Risk Score: 7.2/10 Issues Found: ❌ Missing termination clause ❌ SLA definition unclear ⚠ Liability section weak Confidence: 89% Recommendation: Escalate to Legal Review For low-risk contracts: Contract Type: NDA Risk Score: 2.1/10 Issues Found: ✅ Confidentiality present ✅ Termination clause present Confidence: 94% Recommendation: Approved Contract PDF ↓ Hermes Master Agent ↓ Task Planning ↓ Clause Extraction ↓ Risk Detection ↓ Confidence Scoring ↓ Compliance Check ↓ Final Recommendation 1. Read uploaded contract 2. Identify contract type 3. Extract important clauses 4. Detect missing sections 5. Evaluate business risk 6. Calculate confidence 7. Decide escalation Adding screenshots/video walkthrough soon 🚀 Repository: https://github.com/yourusername/hermes-contract-intelligence-agent Example decision logic: python class ContractDecisionAgent: def should escalate self, risk score, confidence : if risk score 0.7 and confidence 0.8 : return "legal review" return "approved" Hermes Agent sits at the center of the system. Instead of hardcoding a workflow, I used Hermes for: Hermes breaks the task into smaller reasoning steps. Example: Read contract ↓ Determine type ↓ Extract clauses ↓ Evaluate risk ↓ Decide escalation Hermes invokes multiple tools dynamically: parse pdf extract clauses risk detector compliance checker summary generator Different contract types require different reasoning paths, and Hermes dynamically chooses what to do next. The agent doesn't just summarize documents. It reasons through: This felt like a much more realistic enterprise use case for AI agents. One big lesson from building this: Agentic systems become useful only when they can decide what to do next, not just generate text. That’s where Hermes Agent really stood out for me. Thanks for reading 🚀