Blocklog creates a verifiable execution record for AI agents.

AI agents are increasingly making decisions, calling tools, accessing data, and taking consequential actions. When something goes wrong, organizations need to answer:

What happened? Why did the agent do it? What did it know at the time? And can we prove it?

Blocklog captures the execution context required to answer those questions — and makes the resulting record tamper-evident, traceable, and auditable.

Reconstruct AI executions to investigate failures, incidents, unexpected behavior, and disputed decisions.

Trace decisions back through the model, inputs, tools, policies, prompts, workflow state, and actions that produced them.

Record whether critical data was current, stale, or invalid when an AI decision was made.

Create tamper-evident execution records using cryptographic verification, providing an independently verifiable history of consequential AI activity.

Turn production AI executions into structured evidence for security, risk, governance, and compliance workflows.

Enforce authorization and human-approval controls around high-risk agent actions.

Traditional observability tells you that an AI system ran.

Logs tell you what was recorded.

Tracing tells you how requests flowed through a system.

But consequential AI decisions often require something more:

A trustworthy record of the entire execution context.

An AI agent may:

• receive changing external data
• reason over multiple sources
• call tools and APIs
• interact with databases
• invoke other agents
• make policy-sensitive decisions
• trigger real-world side effects

When an incident happens, reconstructing that chain from conventional logs and telemetry can be incomplete or unreliable.

It connects execution → context → decision → action → evidence into one verifiable timeline.

Blocklog is designed for AI systems where decisions and actions matter.

Examples include:

Financial AI

• trading and investment agents
• fraud detection
• underwriting
• payment decisions

Enterprise AI

• autonomous workflows
• internal decision systems
• AI-powered operations

Regulated AI

• systems requiring auditability
• compliance-sensitive workflows
• human oversight requirements

Autonomous Agents

• tool-using agents
• multi-agent systems
• agents capable of taking external actions

Blocklog is built around a few core primitives:

Execution Record Capture the complete context surrounding an agent execution.

Decision Provenance Preserve the relationships between inputs, reasoning context, policies, tools, and resulting actions.

Cryptographic Integrity Make historical records tamper-evident and independently verifiable.

Forensic Reconstruction Turn execution records into a timeline that can be investigated after an incident.

Governance at the Action Boundary Apply authorization and human oversight where agents can produce consequential side effects.

Blocklog is being developed as an engineering-first infrastructure project.

The goal is to make trustworthy AI execution infrastructure accessible to developers building autonomous systems.

Explore the repositories, inspect the implementation, run Blocklog locally, and contribute.

Every consequential AI decision should be explainable, replayable, and auditable.

Blocklog is building the forensic security and governance layer for autonomous AI systems.

🚧 Under active development