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Courses/AI Agents

Intermediate course

Building AI Agents: From Models to Production

Build a production agent one layer at a time.

A hands-on path from a model call to a deployed, evaluated agent across model, harness, tools, skills, and runtime.

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LevelIntermediate
FormatProject-led
Commitment10 modules · 5 weeks
StatusEnrollment open

The course

“Build a production agent one layer at a time.”

Engineers moving from LLM calls to production agents. Every module is organized around applied evidence rather than passive completion.

Curriculum / 10 modules

From first question
to working outcome.

01What is an agent—and when not to build one+

The decision tree before the build.

Lessons
  1. Assistant versus agent
  2. The five layers
  3. When not to build
Field artifactAgent spec and definition of done
02The model layer+

Capability, context, limits, latency, and cost.

Lessons
  1. Reasoning models
  2. Context budgets
  3. Choosing deliberately
Field artifactBaseline planning call with cost
03The harness+

The agent loop, planning, memory, guardrails, and stop conditions.

Lessons
  1. The core loop
  2. Task decomposition
  3. Memory and guardrails
Field artifactAgent loop with memory and stop condition
04Tools+

Designing typed capability surfaces and reliable tool contracts.

Lessons
  1. Tool-surface design
  2. Schemas and descriptions
  3. Server and client tools
Field artifactAgent using an external source
05Skills+

Packaging reusable domain expertise with progressive disclosure.

Lessons
  1. Skill versus tool
  2. Skill structure
  3. Custom domain skills
Field artifactReusable report-writing skill
06Evaluation and testing+

Golden sets, rubric grading, traces, and regression tests.

Lessons
  1. Evaluation discipline
  2. Rubrics and judges
  3. Failures into tests
Field artifactAgent evaluation suite
07Runtime+

Execution environments, state, security, observability, and cost.

Lessons
  1. Execution environments
  2. State across sessions
  3. Security and observability
Field artifactDeployed agent with permission gate
08Multi-agent systems+

When specialists help, coordination patterns, and cost math.

Lessons
  1. When multiple agents help
  2. Coordination patterns
  3. Cost and latency
Field artifactEvaluated multi-agent comparison
09Enterprise patterns+

Retrieval, governance, audit, optimization, and deployment patterns.

Lessons
  1. Knowledge systems
  2. Governance and audit
  3. Cost at scale
Field artifactRetrieval source and audit log
10Capstone—build and ship+

Assemble, harden, evaluate, and demonstrate the complete agent.

Lessons
  1. Final assembly
  2. The hardening pass
  3. Demo and review
Field artifactDeployed research agent and evaluation report

What changes

You leave able to own the whole engagement.

  1. 01Build across all five agent layers
  2. 02Choose agent versus workflow versus single call
  3. 03Write regression-catching evals
  4. 04Ship with permissions, observability, and cost controls

Who it is for

Strong builders moving closer to the problem.

Engineers moving from LLM calls to production agents.

Before you begin

  • Comfortable with Python and REST APIs
  • Have built a basic LLM feature or script

Enrollment open

Start with the first lesson.

Enroll to save progress, resume across devices, and keep your course work connected to your learner profile.

Enroll and begin ↗