AI-agent security and evaluation are converging because tool-using systems can be schema-valid, benchmark-passing, or locally successful while still unsafe across real execution paths.
Evidence
- Prompt injection against coding agents, PlanFlip, agent-memory injection, and Traceforce all identify tool use, planning, MCPs, secrets, and persistent memory as attack surfaces.
- Structured Outputs failures, OrderBench, SAAG, and RAIL Guard show that syntactic validity or exact-match scoring does not guarantee faithful, safe, or executable behavior.
- AgentCompass, BACON, SIFT, micro-F1 moderation analysis, and ‘Who Grades the Grader?’ all push evaluation toward calibrated, decomposed, or governed measurement rather than headline metrics.
- Sentinel, task-tracker agents, production-agent postmortems, and harness-engineering posts show that operational constraints, review states, and end-to-end checks are becoming part of the safety boundary.
Implications
- Production agent deployments will need layered validation: schema checks, semantic checks, tool permissions, trajectory audits, and remediation loops.
- Evaluation systems that drive automation decisions must be calibrated against real operational failures, not treated as neutral score generators.
Concepts
Agents Tool Use LLM Evals MCP Context Engineering Code Agents Agent Memory
Confidence
high