Topic starter
23/06/2026 7:24 pm
TL;DR: An agentic workflow fixed 12 of its 15 flakiest tests over a week and a half, cutting a process that can take an engineer a full day per pair of failures into a 4 to 5 hour loop with no human intervention, according to Kong. The deeper lesson is that runtime autonomy changes how teams should think about debugging, verification, and context management, not just speed.
Editorial analysis by NHI Mgmt Group, based on content published by Kong: “How We Used Agentic AI to Automate Fixes for Flaky Tests”.
Key questions
Q: What breaks when an agentic workflow is given too much context during debugging?
A: The agent starts treating old attempts as current evidence, which can dilute hypothesis quality and slow down resolution.
Q: Why does repeated verification matter more than a single successful rerun?
A: Because flaky behaviour is intermittent, a one-off success proves very little.
Q: How should teams decide which engineering tasks are suitable for agentic automation?
A: Choose tasks that can be bounded, measured and repeated without ambiguity.
Practitioner guidance
- Define bounded agentic task loops Use a narrow identify-fix-verify pattern when assigning autonomous work so the agent has clear inputs, clear exit criteria and a single outcome to prove before escalation.
- Separate investigation from verification Give the investigation step enough depth to form a hypothesis, then use a different verification step to test recurrence rather than trusting the first successful rerun.
- Keep carry-forward context minimal Pass only the summary of previous attempts into the next agent run so earlier dead ends do not crowd out fresh analysis or inflate the context window.
Bottom line: Agentic workflows can materially reduce the time needed to resolve flaky tests when they are tightly bounded and verified in stages.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
25/06/2026 3:23 am
Runtime autonomy changes engineering workflows by collapsing the human babysitting model. This article is not about identity security in the narrow sense, but it is highly relevant to the governance of autonomous systems because the agent is making decisions inside a bounded software task without waiting for human approval between steps. That shifts the control problem from manual oversight to delegated execution design. Practitioners should read this as evidence that autonomous behaviour can be operationally useful only when the task boundary is narrow and the verification rule is explicit.
A few things that frame the scale:
- 88% of organisations have embedded AI agents in their workflows, according to KPMG's 2026 report.
A question worth separating out:
Q: Should human review disappear once an agent has prepared a fix?
A: No. Human review still matters at the point where the change is promoted into the shared codebase, especially when the workflow is autonomous during investigation and verification. The agent can accelerate diagnosis, but governance should still control the final merge decision.
👉 Read our full editorial: Agentic AI for flaky tests: what Kong’s workflow reveals