Controlled agent operations

Production agent teams can add a deterministic replay step to incident response for failures caused by changed facts. StaleTrace describes a narrow design: ingest the agent’s tool calls plus recorded fact events, replay those facts into a temporal ledger with validity windows, and compare what the agent used with what was valid at that moment. The output is a root cause, blast radius, and incident report.

The first useful build is an adapter for one production agent that exports tool calls, customer or account state changes, and timestamps into a replayable log. Teams should test it on a small set of past incidents where the current database state hid what the agent saw at decision time. The public claim to copy is the deterministic audit path: no LLM calls, no embeddings, and the same inputs producing the same verdict. The missing measurement is whether the replay catches real stale-state failures faster than the team’s current trace and log review.

Security teams can trial an agentic static audit as a release gate for code paths that ordinary SAST and live testing often miss: authorization chains, feature-flagged flows, admin routes, mobile app logic, and multi-repository changes. Aikido Code Audit claims to follow references across files and modules, return root cause and code evidence, and generate an AutoFix pull request.

A practical test is a two-week audit on significant releases before merge. Route only findings with code evidence into developer triage, then measure confirmed issue rate, duplicate rate, fix time, and overlap with the latest pentest or manual review. Aikido reports early users finding a median of about 25 issues per codebase and claims 70–80% coverage of what a full pentest surfaces at about one-tenth the cost, but the excerpt gives no public dataset or reproducible protocol. Treat the vendor numbers as a reason to run a controlled release-gate trial, with false positives and fix acceptance measured locally.

Teams with high inference bills for document, email, and spreadsheet assistants can test a router that sends routine work to a smaller model and difficult work to a frontier model, then keeps that model fixed for the session. The reported GDPVal-AA setup routes between GPT-5.5 and GPT-5.4 Mini, reaches 1759 ELO versus 1769 for GPT-5.5 alone, and keeps routing overhead below $0.01 per request.

The build should be small: classify task type and difficulty, choose the model once at session start, log cost, latency, cache hit rate, and user correction rate, and run a blind review on a sample of final outputs. The session lock matters because mid-session model swaps can break prompt caches and change output behavior. The evidence also points to domain-tuned small models as candidates for specialized workloads, including MAI-Code-1-Flash at about 5B active parameters scoring 51.2% on SWE-Bench Pro versus 35.2% for Claude Haiku 4.5 while using up to 60% fewer tokens.