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Design complex agentic workflows: DAG-based task graphs, conditional branching, human-in-the-loop checkpoints, and error recovery paths.

Build automated test suites that exercise agent behavior across edge cases: adversarial inputs, tool failures, multi-turn conversations, and recovery.

Design secure execution environments for AI agents: Docker sandboxes, API key isolation, network policies, and resource limits.

Craft detailed agent personas with expertise areas, communication styles, decision frameworks, and behavioral boundaries.

Design first-run experiences and onboarding flows for AI agents: capability discovery, permission grants, and initial task routing.

Implement tracing, logging, and monitoring for AI agent systems: LLM call traces, tool execution logs, latency tracking, and replay debugging.

Design seamless handoff protocols between specialized agents: context transfer, state serialization, and recovery on failure.

Design self-improving agent systems: outcome tracking, reward signal extraction, prompt refinement cycles, and human preference collection.

Use when an agent workflow needs measurable quality: build task-based evals with graders, baselines, and A/B comparisons before shipping changes.

Design and run comprehensive eval suites for AI agents: task completion rates, tool call accuracy, cost tracking, and regression testing.

Analyze and reduce AI agent operating costs: model routing, prompt caching, context window management, and batch processing strategies.