Your AI coding agent just deleted a production config file. Who told it to? When? Can you prove it?

If you’re using Claude Code, Cursor, or Copilot with any level of autonomy, you already know the uneasy feeling. These agents can read, write, and execute anything on your machine. When something breaks at 2 AM, the only evidence is a chat log that nobody saved.

The accountability gap in AI-assisted development

The rise of autonomous coding agents has created a fundamental governance problem. Traditional version control tracks what changed in code, but not why an agent made a decision, who prompted it, or whether the chain of events has been tampered with after the fact.

This matters more than most teams realize. In regulated industries – finance, healthcare, defense – “the AI did it” is not an acceptable incident report. Even in startups, when an agent modifies infrastructure or leaks a secret, you need a forensic trail that holds up under scrutiny.

InALign: cryptographic proof, not just logs

InALign is an open-source MCP server that embeds directly inside your AI agent and records every action into a SHA-256 hash chain. Each record is cryptographically linked to the previous one. Modify any record and the chain breaks – immediately detectable, mathematically provable.

The setup is deliberately minimal:

pip install inalign-mcp

One command. Restart your editor. Every agent action is now recorded with tamper-proof provenance.

What makes it different

Most logging solutions treat AI agent monitoring as a variation of application logging. InALign takes a fundamentally different approach:

Hash chain integrity – Every action links to the previous via SHA-256. This is the same principle behind blockchain, applied to agent behavior. You cannot alter history without detection.

GraphRAG risk analysis – Instead of simple pattern matching, InALign builds a knowledge graph of agent behavior and uses graph-based reasoning to detect data exfiltration attempts, privilege escalation patterns, and suspicious tool chains.

Runtime policy engine – Three presets (Strict Enterprise, Balanced, Dev Sandbox) that can be switched at runtime. You can simulate a policy against historical events before deploying it – know exactly how many actions would be blocked or flagged.

16 MCP tools, zero configuration – Once installed, your agent automatically gains provenance recording, audit reports, risk analysis, behavior profiling, and policy management capabilities.

The architecture bet

InALign runs locally inside your agent process. Only provenance metadata (action names, hashes, timestamps) flows to the cloud API for graph storage and analysis. Your code and credentials never leave your machine.

This is a deliberate design choice. In an era where developers are increasingly wary of sending code to third-party services, InALign keeps sensitive data local while still enabling centralized audit and risk analysis through its Neo4j-backed graph database.

It works with any MCP-compatible agent: Claude Code, Cursor, Windsurf, Continue.dev, Cline, or custom implementations.

Who needs this

The immediate use case is enterprise teams deploying AI coding agents at scale. When 50 developers each have an autonomous agent modifying production code, “trust but verify” becomes “verify or regret.”

But the broader implication is more interesting. As AI agents move beyond coding into operations, customer support, and decision-making, the need for tamper-proof audit trails becomes universal. InALign’s MCP-based approach positions it to follow agents wherever they go.

What to watch

The project is fully open-source under MIT license and available on PyPI. The team is building toward blockchain anchoring on Polygon for ultimate immutability – anchoring hash chain checkpoints on-chain so that even the InALign server itself cannot retroactively alter records.

For teams evaluating AI agent governance, this is worth a serious look. The window between “AI agents are useful” and “AI agents need auditing” is closing fast.

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