Topic starter
20/08/2026 5:27 pm
TL;DR: 79% of organisations have adopted AI agents, showing why Braintrust's survey treats observability as evidence for governing multi-step behaviour. Without trace-level evaluation, teams cannot explain regressions or prove an agent is reliable before release.
Editorial analysis by NHI Mgmt Group, based on content published by Braintrust: “5 best AI agent observability tools for agent reliability in 2026”.
Key questions
Q: Why does traditional monitoring fail to explain AI agent mistakes?
A: Traditional monitoring is usually built for deterministic systems, so it records symptoms like latency or error counts rather than the reasoning path that produced the result.
Q: How do teams know if AI observability is actually working?
A: It is working when teams can show which change caused a quality shift, which dataset surfaced the issue, and whether the regression was contained before users were affected.
Q: How should teams evaluate AI agents that make multi-step decisions?
A: Teams should use trace-aware evaluation when an agent’s failure can occur across tool calls, retries, or intermediate state changes.
Practitioner guidance
- Define evaluation gates for production agents Use automated scoring for accuracy, relevance, safety, and task completion before changes reach users.
- Capture full multi-step traces Instrument each tool call, model response, prompt, and latency step so failures can be reconstructed end to end.
- Align observability to release control Require the same metrics in testing, CI/CD, and production so regressions are measured consistently across environments.
Bottom line: AI agent observability is moving into the control plane because multi-step agent behaviour cannot be governed with traditional monitoring alone.
Glossary terms: AI Agent Observability, Evaluation Score, Identity Traceability, Agentic workflow
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This topic was modified 2 days ago by NHI Mgmt Group
20/08/2026 6:16 pm
AI agent observability is becoming a governance control, not a monitoring feature. Once agents can chain tools, reason across steps, and act inside business workflows, the control question changes from 'did the system respond?' to 'can the organisation explain and validate what it did?' That is a governance problem as much as an engineering one, and it belongs in release approval, risk review, and accountability reporting.
A question worth separating out:
Q: Should organisations prioritise evaluation before expanding agent deployment?
A: Yes. Evaluation should come before scale because it is the mechanism that tells you whether the agent is reliable enough for customer-facing or business-critical use. Without evaluation gates, teams may see the system work in demos while missing failure patterns that only emerge under real workflows, real data, and repeated use.
👉 Read our full editorial: AI agent observability is becoming a governance control, not just monitoring