AI Agent Developer
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- Build grounded LLM workflows and reliable tool-use primitives before adding autonomy
- Engineer stateful agents with MCP, planning, memory and multi-agent coordination
- Evaluate, secure and operate agent systems with trace-driven reliability and production controls
Benchmark: OpenAI Agent Post-Training + Anthropic Claude Managed Agents (current Sep 2026) + Stanford CS329Z Engineering AI Agents and CS224V Agentic AI (Fall 2026) Market refresh: context engineering, retrieval, tool/function calling, MCP, planning and state, memory, agent loops, multi-agent orchestration, evals, trace debugging, sandboxing/permissions, reliability, cost/latency and production operations.
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Build grounded LLM workflows and reliable tool-use primitives before adding autonomy
LLM behavior + context engineering foundations
Coverage: Prompt/Context Engineering / Structured Outputs / Reasoning / Task Decomposition
Grounding, RAG + retrieval-backed action
Coverage: RAG / Retrieval / Citations / Verification / Knowledge Interfaces
Engineer stateful agents with MCP, planning, memory and multi-agent coordination
Tool/function calling + MCP integrations
Coverage: Agent SDKs / Function Calling / MCP / Permissions / Tool Schemas
State, planning + multi-agent orchestration
Coverage: Agent Loops / Memory / Planning / Handoffs / Multi-Agent / Human Approval
Evaluate, secure and operate agent systems with trace-driven reliability and production controls
Evals, trace debugging + regression gates
Coverage: Task-Success Evals / Trajectories / Graders / Failure Analysis / Regression
Safety, reliability + production AgentOps
Coverage: Sandboxing / Least Privilege / Observability / SLOs / Cost-Latency / Rollback
Role-Readiness Proof / Capstone
Run a 7-day Agent Developer Lab: define a real workflow and success criteria; build a Python/TypeScript agent with RAG, at least three tools and one MCP integration; implement explicit state/memory and human approval for risky actions; add tracing and a 50+ case eval set; test tool failures, prompt injection and permission boundaries; measure task success, latency and cost; add regression gates, SLOs, fallback/rollback and an incident runbook; demo a multi-step production task.
Proof Rubric
- Useful autonomous behavior, not a scripted demo
- grounded context
- correct tool schemas and authorization
- state/memory discipline
- bounded autonomy and human controls
- reproducible eval set
- trace-based failure analysis
- injection/sandbox tests
- measurable task success, latency and cost
- production observability
- safe fallback/rollback
- clear engineering tradeoffs