About This Course
Welcome to the "Al Agent - Harness Engineering for Production Multiagent Systems" course, an advanced, hands-on program for senior software engineers, Al architects, DevOps/MLOps professionals, and technical consultants who need to move Al agents out of the experimental sandbox and into high-stakes production. It is ideal for developers experiencing agent instability who want to build the protective system infrastructure - the "harness" - required to guarantee reliability, safety, and cost control. Over three days you will transition from basic prompt design to mastering full-lifecycle Harness Engineering for production-grade Al agents. You will construct robust inner and outer system loops, implement rigorous evaluation frameworks, deploy multilayered guardrails against prompt injection and tool abuse, manage runtime context windows to prevent silent failures, and design controlled multi-agent systems - culminating in a capstone where you design a production-ready agent harness end to end.
Learning Objectives
Upon completing this course, you will be able to:
- Explain the shift from prompt engineering to Harness Engineering, and why prompts alone are insufficient for production agents.
- Apply prompt and context engineering for agentic systems, including context-window management (include, summarize, compress, drop).
- Distinguish the inner loop (a single run) from the outer loop (across runs), and design each for behavior, reliability, safety, and cost.
- Design safe tools with permissions, validation, sequencing, failure handling, and approval gates for high-risk actions.
- Build evaluation harnesses with golden datasets, scenario, adversarial, and regression tests, and LLM-as-judge scoring
- Implement observability and tracing for prompts, context, tool calls, state, latency, token usage, and cost, and perform root-cause analysis.
- Deploy multi-layered guardrails and governance: input/output controls, role-based access control, least-privilege tools, and human-in-the-loop.
- Design controlled multi-agent harnesses using supervisor, router, specialist, and reviewer patterns.
- Apply the Harness Engineering maturity ladder to evolve agents toward production readiness.
- Deliver a capstone: design a production-ready agent harness covering architecture, evaluation, observability, guardrails, CI/CD, and deployment readiness.
Prerequisites
- Own laptop with sufficient RAM and disk space, preinstalled with Python, Node.js, Git, and Docker Desktop for the hands-on labs.
- Some signups for online tools will be required, using a Google account.
- Prior experience building Al agents or LLM applications - this is an advanced-level course.
- Strong Python skills (functions, classes, JSON, APIs) and comfort with the command line.
- Familiarity with LangGraph, LangSmith, or similar agent frameworks is recommended.
Target Audience
- Senior software engineers moving Al agents from prototype into high-stakes production.
- Al architects designing reliable, safe, and cost-controlled agent systems.
- DevOps/MLOps professionals responsible for deploying, monitoring, and operating agents.
- Technical consultants addressing agent instability with production-grade infrastructure.
- Engineering leads building evaluation, observability, and governance into multi-agent systems.
Training Outline
- From Prompt Engineering to Harness Engineering
- Prompt and Context Engineering for Agentic Systems
- Inner Loop and Outer Loop of Al Harness Engineering
- Tooling, Actions, and Control Boundaries
- Evaluation Harness for Agents
- Observability, Tracing, and Debugging
- Guardrails, Security, and Governance
- Multi-Agent Harness Design
- Harness Engineering Maturity Ladder
- Capstone - Building a Production Agent Harness