Agentic AI for Builders
Decide whether a use case needs an agent, build and deploy it, then evaluate, secure and operate it with evidence.
Is this your path?
This path is for developers, data and AI engineers, solution architects and technical leads who are responsible for designing, integrating, deploying and operating systems that use AI agents.
If you manage the process but do not build the system, see the Managers path. If you want agents to help with your own work, see the Professionals path.
- Judge whether a use case needs an agent, a model, a rule or something simpler
- Build and deploy an agent with tools, permissions, memory and recovery
- Evaluate, secure and operate it, with evidence for the release decision
Your three levels
Start at Level 1 to assess a use case before you build. If you already have a scoped use case and can code, Level 2 is a good start. You do not need to take all three.
Level 1 · 16 hours · Understand and chooseAgentic AI Feasibility and Architecture
Check whether a use case is feasible and needs an agent at all, then plan the architecture before anyone builds.
A technical opportunity backlog, a feasibility assessment, a system-context diagram, the main risks and architecture options.
- Workflow or agent? Choosing the simplest approach that works
- Scoping a use case and checking data readiness
- Planning the architecture and integration points
- Build, buy or partner, and the path from prototype to production
- Typical agents we assess: lead qualification, customer onboarding, proposal drafting, meeting preparation, knowledge-base search and FAQ automation
Level 2 · 24 hours · Apply and buildAgentic AI Development and Deployment
Build an agent that works with real tools, permissions and data, and get it ready for release.
A working or near-working agent, its architecture and integration design, a permission plan, a deployment brief, acceptance criteria and an initial test suite.
- Connecting agents to tools, data and APIs
- Permissions and access control for agents
- Memory, retrieval and single or multi-agent design
- Guardrails, recovery and getting ready for release
Level 3 · 24 hours · Test and controlAgentic AI Security, Evals and Operations
Test an agent properly, secure it against misuse, and run it in production with monitoring and a way back.
An evaluation suite, security evidence, a release decision, a monitoring and alert plan, an incident and rollback plan, and an operating-ownership and lifecycle record.
- Testing and evaluating agents before and after release
- Security risks such as prompt injection and excessive agency
- Monitoring, logs and audit evidence
- Rollback, incident response and keeping agents maintained
Function and sector editions
The same method, applied to one industry with its own examples and controls.