AI Agent Engineering Bootcamp Certificate
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Bootcamp Mastery Certificate B09
Primary spine: Microsoft Developer AI Agents for Beginners, reinforced with a current Microsoft AI Dev Days production bridge. Build agents that use tools, retrieve grounded context, plan, coordinate with other agents, improve through evaluation and operate safely with observable production boundaries.
Benchmark: Microsoft publishes AI Agents for Beginners and AI Dev Days as public learning videos. Zuyini curates those videos and applies its own independent build-and-proof completion rubric; this does not imply a Microsoft-issued certification or endorsement.
Core Video Sequence
01 — Complete 10-lesson AI Agents course
02 — Choose an agent framework deliberately
03 — Design effective agent workflows
04 — Tool use and action boundaries
05 — Agentic RAG and grounded retrieval
06 — Planning and task decomposition
07 — Multi-agent systems and coordination
08 — Agent improvement, memory + evaluation
09 — Deploy agents into production
10 — Current bridge: orchestration, MCP + observability
Build-Along Checkpoints
- Checkpoint 1: Build a single-agent baseline with explicit goal, instructions, structured state and deterministic task-success checks.
- Checkpoint 2: Add two tools with schemas, validation, least-authority permissions and graceful handling for invalid or unavailable calls.
- Checkpoint 3: Add agentic RAG or grounded context with source tracking; create cases that distinguish retrieval failures from reasoning failures.
- Checkpoint 4: Implement planning or workflow orchestration; compare a fixed workflow against a more autonomous strategy on the same task set.
- Checkpoint 5: Add a second specialist agent or MCP server; define handoff rules, shared state and one human-in-the-loop approval boundary.
- Checkpoint 6: Instrument traces, latency, cost and task outcomes; inject a tool/model failure, diagnose it and demonstrate safe recovery.
7-Day Independent Capstone
- Day 1: Choose a real multi-step workflow; define users, success criteria, prohibited actions, tools, trust boundaries and escalation paths.
- Day 2: Build the baseline agent and a task/eval set; measure task success, groundedness, tool correctness, latency and cost.
- Day 3: Add tool use with typed schemas, authorization checks, retries/timeouts and a documented failure taxonomy.
- Day 4: Add grounded retrieval plus planning/orchestration; run adversarial and ambiguous cases and record the failure analysis.
- Day 5: Add multi-agent or MCP integration with bounded delegation, shared-state rules and human approval for a consequential action.
- Day 6: Deploy with traces/telemetry; simulate a tool outage, prompt injection or bad retrieval event and show containment/recovery.
- Day 7: Publish repo, architecture, eval report, trace screenshots/export, threat-boundary note, demo video and operations runbook.
AI Agents - Build, Evaluate & Operate
Ship an agentic system that can reason over bounded context, call tools, recover from failures and prove task success. Completion requires explicit tool permissions, a planning/orchestration strategy, agent evaluation, traces and an operational runbook; advanced learners should add multi-agent coordination or MCP.
Proof Rubric — What Must Be Shown
- TASK SUCCESS: Explicit eval set + measurable completion
- TOOLS: Schemas, auth + safe failure handling
- GROUNDING: Retrieval evidence + source discipline
- PLANNING: Workflow/autonomy tradeoff evidence
- MULTI-AGENT: Handoffs, state + bounded delegation
- EVALS: Regression, failure taxonomy + comparison
- OBSERVABILITY: Traces, latency, cost + incident proof
- SAFETY: Permissions, HITL + recovery boundaries