An autonomous agent is a state machine that happens to consume natural language plans. Verification therefore spans steps: precondition checks before tool emission, invariant checks across multi-hop traces, divergence detection versus learned baselines, and post-condition scanning on payloads returned to humans or downstream services.
Beyond static test suites
Unit tests freeze behaviors; attackers mutate prompts and tool payloads continuously. Runtime verification observes live traces and compares signatures—tool sequence histograms, argument entropy, unusually parallel fan-out—to policy expectations. Alerts should carry compact evidence summaries so on-call responders don’t stare at opaque embedding distances.
Contract surfaces worth instrumenting
- Tool manifests: enforced JSON schema, enumerated hosts, egress allow listing, IAM-scoped identities per agent—not shared service accounts.
- Memory checkpoints: versioning of summaries injected back into prompts; poisoning here is stealthy.
- Human escalation edges: deterministic triggers when autonomy budget (steps, USD, scopes) crosses thresholds.
- Cross-agent delegation: delegated tasks inherit parent lineage IDs for causal tracing.
Pair verification with reproducible timelines
When SOC asks “why did Agent 7 retrieve customer payroll last Tuesday?”, timelines must stitch gateway verdicts, MCP round trips, and model completions without manual grep. Replay becomes possible only if each hop logs structured hashes of inputs—not raw payloads where regulation forbids—but enough to correlate events across stores.
Intertrace’s bias is pragmatic: classify fast, cite policy, retain normalized detections engineers can alert on later, and keep verification overhead within single-digit milliseconds for hot paths—with heavier checks scheduled or sampled when necessary.