// The system of record for AI decisions
Blocklog captures the complete evidence trail behind every AI decision — inputs, retrievals, tool calls, policies, approvals, outputs. Explainable to engineers. Auditable by compliance. Governable by executives.
Decisions recorded
Signed, immutable, replayable
Time to first record
Shadow mode, zero production changes
Decision record
dec_9af1c18Every field that existed when this decision was made. Signed. Permanent. Replayable on demand.
// The Accountability Gap
Every database write is logged. Every API call has a trace. Every code change has a commit. Software infrastructure has spent thirty years building accountability into every layer of the stack. Then AI arrived — and the most consequential outputs your systems now produce have no permanent evidence layer behind them. A customer complains. A regulator asks. An auditor requests documentation. Nobody can prove what happened.
For Engineers
The decision that surfaced at step 12 was caused at step 3. Blocklog shows you the causal chain across the entire workflow — not a stack trace, not a log dump. Which input was stale. Which policy was active. What would have changed the outcome.
For Compliance
The same evidence your engineer used to debug the failure is formatted as a signed forensic report. Decision ID, agent fingerprint, policy version, approval lineage, regulatory mapping. Readable by an auditor without a technical translation layer.
For Executives
Every AI decision your company has made is now searchable, explainable, and attributable. Not as a debugging tool. As infrastructure. The permanent evidence layer behind every outcome your AI systems produce.
// What Gets Captured
Blocklog runs in shadow mode — parallel to your agent, touching nothing in production. It captures every layer of every decision: not just what the agent output, but what it was working with, which policies were active, which humans approved, and what would have changed the outcome.
Inputs
The exact information available when the decision was made, including freshness and provenance.
Retrievals
The documents, knowledge sources, and context the model actually used.
Tool Calls
Every external action, response, and dependency involved in the workflow.
Governance
The rules, controls, and requirements active at the moment of evaluation.
Approvals
Human reviews, overrides, escalations, and approval lineage.
Decision
The final outcome preserved with its complete evidence trail.
// Forensic Replay
The failure that surfaced at step 12 was caused at step 3. A 22-second input staleness difference was the entire causal factor. Blocklog shows you the complete causal chain — not a stack trace, not a log grep. The exact reconstruction of what the model was working with when it decided.
Inputs
The exact data available when the decision was made.
Execution
Every action taken by the AI system during evaluation.
Governance
Policies, thresholds, and human approvals that influenced the outcome.
Outcome
The final decision, preserved with its complete evidence trail.
// Prove What Happened
Blocklog does not generate compliance reports separately from debugging data. They are the same record. The engineer uses it to understand the failure. The compliance officer uses it to respond to the auditor. The executive uses it to demonstrate governance. One system of record. Three audiences. No duplication.
Forensic decision report
auto-generatedGenerated automatically from production data. No engineer in the room. Readable by compliance without a technical translation layer.
// Why Existing Tools Fall Short
Traces and logs were designed for debugging distributed systems — not for proving what an AI system was working with when it made a consequential decision. They are different problems. Blocklog is not a better trace. It is a different category.
Observability
What they do
Show how systems execute. Traces, logs, metrics, latency, failures, and performance.
What they miss
They were built for software systems. AI systems make decisions. Observability explains execution. It does not preserve accountability.
AI Tracing
What they do
Capture prompts, completions, tool calls, and agent workflows during development and production.
What they miss
They help teams understand what happened today. They were not designed to prove what happened six months later to an auditor, regulator, or customer.
Logs & Data Lakes
What they do
Store large volumes of operational data and make it searchable.
What they miss
The evidence required to explain an AI decision is fragmented across systems. Reconstructing the decision later becomes a manual investigation.
// How Adoption Works
No procurement approval required to start. No sales call required to see value. The path from shadow mode to enterprise contract runs through the product itself — because the same data that makes engineers faster makes compliance teams defensible and executives accountable.
01
Step 01
Blocklog records the complete evidence trail behind every AI decision.
02
Step 02
Reconstruct the exact decision, inputs, approvals, and execution path in seconds.
03
Step 03
See the causal chain, policy evaluations, and factors that influenced the outcome.
04
Step 04
Generate regulator-ready evidence months after the decision occurred.
05
Step 05
Turn every AI decision into a permanent, auditable system of record.
Shadow mode. Zero production impact. No procurement approval. Every decision becomes explainable, replayable, and auditable from the moment you install.
The system of record for AI decisions.