AI Agent Repair

Repair AI agents that are failing, drifting, or simply not delivering.

When agents start breaking, slowing down, hallucinating, or returning inconsistent results, the fastest answer is rarely “start over.” Herb Trevathan helps teams isolate root causes and restore performance.

Common repair issues

Repeated tool invocation failures and API errors
Prompt drift, unstable outputs, and weak response quality
Broken decision logic and unreliable orchestration
Latency spikes, retries, and cost blowouts
Hallucinations in business-critical workflows

What Herb focuses on

Root-cause analysis across prompts, tools, routing, and guardrails
Stability improvements for production agent behavior
Short-term fixes and long-term architectural recommendations
Clear operating guidance for technical and non-technical stakeholders

Audit the workflow

Inspect prompts, tools, and handoffs to locate the real failure pattern.

Stabilize the system

Apply targeted changes to improve consistency and reduce production risk.

Prevent repeat failures

Add the monitoring and operating discipline needed to keep issues from returning.

Observe before changing

Monitoring makes agent failures diagnosable.

A reliable operating view connects prompts, tool calls, workflow outcomes, retries, latency, token usage, cost, escalations, and operator interventions. Thresholds and reports should expose degraded quality or broken integrations early enough to act.

Logging

Capture prompt, tool, routing, and outcome context.

Diagnostics

Group failures, retries, drift, and recurring weak cases.

Alerts

Flag degraded quality, broken tools, latency, or cost thresholds.

Reporting

Track reliability and efficiency across releases and workflows.

Improve with evidence

Optimization should follow measured failure and cost patterns.

Reduce avoidable work without weakening the result: tighten context, improve tool sequencing, right-size models, control retries, clarify fallbacks, and route uncertain or high-risk cases to human review.

Lower unnecessary token and infrastructure cost.
Reduce latency and repeated workflow steps.
Improve output consistency, model routing, and fallback behavior.
Connect operating metrics to the business decision the agent supports.

Not sure whether the problem is architecture or model fit?

Use the Choosing a Model page to compare self-hosted model options and get a recommendation based on quality, latency, privacy, and infrastructure expectations.

Explore model selection

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