Topic starter
18/08/2026 2:13 pm
TL;DR: Models can score well on benchmarks and still fail as agents, because production reliability depends on the harness, not the model, according to Fiddler. The article argues that system instructions, context assembly, guardrails, sandboxes, and human escalation points determine whether agents fail safely, especially when tool calls, permissions, and long-tail cases go wrong.
Editorial analysis by NHI Mgmt Group, based on content published by Fiddler: “Building Reliable Agents Takes More Than Better Models”.
Key questions
Q: What breaks when AI agent execution is not isolated from the harness?
A: Untrusted tool calls and generated code can affect the harness itself instead of being contained within a separate execution boundary.
Q: Why do AI models create governance risk even without retraining?
A: Because behaviour can change at inference time when the model sees new context, examples, or instructions.
Q: What are the signs that an AI SOC agent is failing in production?
A: Common warning signs include inconsistent verdicts, rising false positives, missed true positives, and growing dependence on manual investigation.
Practitioner guidance
- Define the agent’s runtime authority boundary Document which tools, data sources, and actions the agent may use without human approval, and cap that scope to the smallest viable workflow.
- Redesign tool interfaces for agent use Remove unnecessary fields, summaries, and responses from APIs that feed agents so the context window only receives task-relevant data.
- Instrument before automating Add logging, traceability, and escalation checkpoints before allowing the agent to perform real work, especially where tool calls can change state.
Bottom line: Agent reliability depends on the harness that surrounds the model, not on benchmark strength alone.
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18/08/2026 3:05 pm
Harness-level control is now the real governance boundary for agentic AI. The article shows that reliability does not emerge from model quality alone, but from the surrounding system that constrains action. That shifts the governance question from 'how good is the model?' to 'what authority does the agent actually have at runtime?' For identity leaders, the important lesson is that delegated access, escalation rules, and tool exposure are part of the control plane. Practitioners should govern the harness as an access boundary, not as an implementation detail.
A few things that frame the scale:
- Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027, citing rising costs, unclear value and insufficient risk controls.
A question worth separating out:
Q: Should organisations hand over more work to AI agents before instrumenting them?
A: No. Instrumentation should come first because observability, logging, and escalation paths are what make delegated action governable. Without them, the team cannot tell whether a failure came from the model, the context, the tool design, or the permissions model.
👉 Read our full editorial: Building reliable AI agents requires harness-level controls