When the regulator calls in six months — or your customer asks why an agent acted — what do you show them? This is what changes when those decisions have structured, tamper-evident memory underneath them.
Right now, when one of your agents acts in production, this is what gets recorded:
Six months later, you need to answer: "What did the trader agent decide about IG credit, and why?"
A regulator's request becomes a forensic exercise. A customer's "why did your AI do this?" gets a shrug.
Wherever your agent is already making a decision, wrap it with a single capture call. MI structures, signs, and stores. No model in the write path — what your agent decides is exactly what gets sealed.
It runs as middleware in your stack. Your existing tools stay where they are — vector store, observability, dashboards. MI is the layer underneath that gives the rest of them a real audit story.
Pick a financial agent context. The simulated agent runs over ~30 seconds. Each decision streams to MI as a real capture against the live API — what you see on the right is what your production stack would record.
Now ask anything about what the agent did. MI does semantic search across the captured record set — not keyword grep, not vector-only — and cites the exact UMO that answered, with score, timestamp, and hash.
Run an agent above first — then this becomes live.
Every captured decision carries a deterministic content hash. The verify endpoint re-computes from stored content and compares to the chain. Tampering — even one byte — breaks the chain. Trust-by-math, not trust-us.
The hash above is computed from the stored content — not assigned by us.
When verify runs, MI re-computes the hash from what's stored and compares it to the chain.
If anyone changed even one character of this record since capture, the chain breaks and
verify returns hash_chain_valid: false. The pipeline is deterministic — same
input always produces the same hash, different input produces a different hash. That's
cryptographic tamper-evidence, not "trust us."