Topic starter
02/08/2026 12:01 pm
TL;DR: Agent testing now has to validate multi-step workflows, tool selection, context retention, and safety controls because traditional single-turn LLM metrics miss real execution failures, according to Openlayer. As 65% of organisations run AI agent pilots and 80% report agents acting outside intended scope, the governance gap is no longer theoretical.
Editorial analysis by NHI Mgmt Group, based on content published by Openlayer: “Agent testing in February 2026: your complete guide to validating AI systems”.
By the numbers:
- 65% now run pilot AI agent programs, nearly double the 37% from the previous quarter.
Key questions
Q: How should security teams test AI agents after prompts, models, or tools change?
A: They should test AI agents every time a meaningful change occurs, not on a calendar alone.
Q: Why do AI agents create more risk than standard LLM evaluations suggest?
A: AI agents create more risk because the dangerous part is often what they do after the response is generated.
Q: What are the signs that an agent workflow is failing even when monitoring looks healthy?
A: Common warning signs include wrong retrieval, repeated retries with no progress, invalid tool arguments, skipped steps, hidden state changes, and false completion claims.
Practitioner guidance
- Build workflow-based agent tests Create test cases that validate complete task journeys, including tool selection, state retention, retries, and termination conditions across multi-turn interactions.
- Add security checks to agent evaluation Include prompt injection, unauthorized API calls, and data exfiltration scenarios in the same test suite used for functional validation.
- Calibrate LLM-as-judge outputs Use human-reviewed datasets, multiple judge models, and periodic spot-checks so the evaluator is measured against expert-reviewed outcomes.
Bottom line: Agent testing has to assess workflow integrity because agents can appear correct while failing on tool use, memory, or safety boundaries.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
02/08/2026 1:07 pm
Workflow validation is now an identity control problem, not just an AI quality problem: once an agent can invoke tools, the evaluation target becomes delegated action, not fluent text. That means the real question is whether access, state, and authorization boundaries hold across a full task path. Teams that still judge agents on isolated prompts are measuring the wrong thing. Practitioners should move evaluation closer to issuance-time control and execution tracing.
A few things that frame the scale:
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
- Gartner predicts that by the end of 2026, 40% of enterprise apps will feature task-specific AI agents.
A question worth separating out:
Q: How can teams govern prompt injection in agents with tool access?
A: They need validation that covers manipulation attempts, scope violations, and unsafe downstream actions before the agent reaches external systems. The practical test is whether the agent rejects unauthorized requests, blocks sensitive data access, and stops suspicious calls before they can affect APIs or databases.
👉 Read our full editorial: Agent testing now needs workflow validation, not prompt scoring