The only agentic engineering certificate built for production. Your engineers come back ready to ship multi-agent systems the business can trust.
For people who already build software professionally and now move into agentic AI engineering: designing, building and operating systems that act on their own, with someone accountable when they do.
Developers who ship production code and want to move into agentic engineering: agents that plan, call tools and hold memory rather than a clever prompt chain.
The people who will own the agentic engineering decisions, the guardrails and the answer when someone asks whether it is safe to launch.
Whoever runs it afterwards: cost, latency, observability, and the escalation path when an agent gets something wrong.
Three modules of agentic engineering in practice: foundations, orchestration and production, each closing with a lab on the same system, so the final review looks at something real.
What actually changed when a model stopped answering and started acting, and why it changed now.
Learning objectiveTell an agent from a chatbotKnow which problems need an agent at all, and which a single model call already solves.
Certify Your TeamPlanner, memory, tools and executor — the four parts, what each one is responsible for, and where they break.
Learning objectiveDesign an agent architectureTurn a real use case into an agent design the team can build and defend.
Certify Your TeamReactive, planning and reflective loops, and how to choose between them for a real use case.
Learning objectiveChoose the right agent patternThe judgement to match the loop to the problem, not the problem to the loop.
Certify Your TeamImplementation patterns for tool use, with error handling and retries that hold under load.
Learning objectiveImplement reliable tool useTool calls that fail safely, so one bad response never takes the system down.
Certify Your TeamBuild one end to end, evaluated against a rubric before the session closes.
Learning objectiveShip a first working agentA foundation the team extends in the next module, rather than a demo that gets thrown away.
Certify Your TeamWhat MCP is, how it is architected, and when reaching for it beats writing another integration.
Learning objectiveConnect agents through MCPOne standard way in to the tools and data your agents need.
Certify Your TeamWiring an agent to external tools, data and services through MCP rather than through glue code.
Learning objectivePut agents to work on real systemsAgents that act on the team's own services and data, not on sample APIs.
Certify Your TeamOrchestrator and sub-agent, pipeline, and blackboard — what each pattern costs and what it buys.
Learning objectiveCoordinate several agentsSplit one job across agents in a way that is cheaper and clearer than one agent doing it all.
Certify Your TeamShort-term buffers against persistent stores, and who owns what when several agents write.
Learning objectiveKeep shared state consistentSeveral agents working one task without overwriting each other's work.
Certify Your TeamTwo agents on one shared task, plus a scenario-based orchestration quiz.
Learning objectiveDebug coordination failuresA systematic method for the failures that only appear once more than one agent is involved.
Certify Your TeamInput and output validation, and deciding exactly what an agent is allowed to do on its own.
Learning objectiveScope authority and guardrailsClear limits on what each agent may do, set before launch rather than after an incident.
Certify Your TeamCheckpoints and escalation paths, so the system asks a person at the moments that matter.
Learning objectiveDesign escalation that worksPeople brought in when judgement is needed, without slowing everything else down.
Certify Your TeamWatching what agents spend and how long they take, and seeing inside them when something goes wrong.
Learning objectiveRun agents in productionKnow what the system costs and why it failed before a user has to tell you.
Certify Your TeamThe checklist and the launch review that decide whether this goes live.
Learning objectiveJudge readiness to launchCriteria the team can apply on its own to every agent it ships next.
Certify Your TeamFinalise the multi-agent system and present it as a production-readiness review.
Learning objectiveDefend the system in a launch reviewPresented to the standard a real go‑live decision would demand.
Certify Your TeamAgentic AI engineering is learned by doing: every module closes with a lab where engineers build, break and fix the system themselves.
The same multi-agent system is built and iterated across every session, so every exercise builds on the last.
Scoped authority, validation and escalation are designed into the system, not bolted on after the demo works.
The program ends in a readiness review, which is the conversation that actually decides whether something launches.
Two checkpoints against a rubric during the program, then a capstone presentation and a written guardrail exercise.
An Agentic Engineering Certification per participant, issued for the checkpoints passed and the capstone defended.
Agentic AI engineering end to end: the anatomy of an agent, planning and reflection patterns, tool use through MCP, orchestration across several agents, shared memory, guardrails and production readiness.
21 hours in total across three modules, instructor-led on site or live online and scheduled around your team.
Engineers and technical leads who already build software professionally and are moving into agentic engineering: from prompting models to operating multi-agent systems in production.
MCP is how multi-agent systems reach real tools and data. It is built into the agentic engineering program rather than mentioned, because an agent without tools is a demo.
A multi-agent system the team keeps running, and the Agentic Engineering Certificate for those who pass the checkpoints and the capstone.
The agentic engineering training is built for the group and priced on team size, level and timeline.
Strategy, workforce design and the economics of an AI-native organisation — for the people who set direction.
Learn More →Bridge product and engineering on AI features — from the PRD through the metrics to the ethics.
Learn More →21 hours redesigning how a team works — specs, documents, meetings, and a workflow proposal with its impact estimate.
Learn More →21 hours governing AI across the development lifecycle — policy, oversight, licensing risk and a rollout plan.
Learn More →Pilots do not pay for themselves. Multi-agent systems that reach customers do. Your team builds one end to end in your own environment, and keeps everything it needs to do the next one alone.