Skip to Content
View all events

Harness Engineering for AI Agents

Published by O'Reilly Media, Inc.

Intermediate content levelIntermediate

Design and build production-ready agentic infrastructure

What you’ll learn and how you can apply it

  • Design a memory-first agent harness by selecting the appropriate memory types, substrates, and harness components for a given application mode
  • Build a working assistant agent harness that integrates working, episodic, semantic, and procedural memory with tools, Skills, MCP servers, and a sandbox environment
  • Implement a semantic cache at the harness boundary and measure its effect on latency and cost
  • Instrument an agentic application with the observability needed to trace memory reads, memory writes, and agent decisions in production

Course description

A 2026 industry survey found that 88% of AI agent projects never ship, and the bottleneck is almost always the same. The agent runs, but the system wrapping the agent is missing, brittle, or stitched together from patterns that worked in a notebook and fell apart under real traffic.

The discipline that addresses this is harness engineering. The formulation “agent = model + harness” captures the idea: the model reasons, and the harness does everything else.

In this two-hour course, Richmond Alake teaches you to design and build a harness around an agentic assistant. You’ll learn the six components of a general agent harness, the four memory types that any production agent needs, and how to decide which memory substrates fit your use case. You’ll assemble a working assistant harness with all four memory types wired in, extend tool and MCP capabilities, configure a sandbox, and add a semantic cache at the harness boundary. Finally, you’ll walk through a harness-oriented full stack application, connect it to a frontend, and instrument it with the observability needed to debug agent behavior in production.

This live event is for you because...

  • You’re a software, ML, or AI engineer who’s built agent prototypes and wants to make them production-ready.
  • You’re a technical lead or architect evaluating how to structure your team’s agent infrastructure.
  • You work with frameworks like LangChain, LangGraph, CrewAI, LlamaIndex, or the LlamaIndex and want a deeper mental model for what sits around them.
  • You want to become a harness engineer at companies building agent-powered products.

Prerequisites

  • A Python 3.11+ environment with a recent agent framework (LangChain, LangGraph, or the Claude Agent SDK) installed on your computer
  • An API key for at least one frontier model provider (Anthropic, OpenAI, or equivalent)
  • A local database available for memory persistence (Oracle AI Database)
  • A code editor with notebook support
  • Git installed for cloning the course repository (shared before the course)
  • A working knowledge of Python, including async patterns and basic API development
  • Familiarity with LLM APIs and prompt-level agent behavior (tool calling, system prompts, structured outputs)
  • Exposure to at least one agent framework or SDK (helpful but not required)

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Foundations of harness engineering and memory-first architecture (40 minutes)

  • Presentation: Why the harness determines production reliability and the model-context-harness-agent layering; the six components of a general agent harness and agent architecture choices (covering single-agent, orchestrator-worker, and parallel); the four memory types; why memory allocation precedes every other harness design decision; matching memory and components to application modes
  • Q&A
  • Break

Assembling the harness for an agentic assistant (45 minutes)

  • Presentation: Provisioning memory substrates; comparing filesystem interfaces with database storage and deciding when to use each; implementing working, episodic, semantic, and procedural memory for assistant mode; tools, MCP servers, skills, sandbox configuration, and semantic caching at the harness boundary
  • Hands-on exercise: Build the harness building blocks step by step, provisioning the memory substrates, instantiating each of the four memory types, registering tools and MCP servers, configuring the sandbox, and wiring in the semantic cache
  • Break

Shipping a full stack agentic assistant (35 minutes)

  • Presentation: The anatomy of a harness-oriented full stack application; a code walkthrough of the files that determine agent behaviour; connecting the agent loop to a frontend surface with observability for memory reads, memory writes, and execution traces
  • Hands-on exercise: Extend the assistant into a deployable full stack app and trace a session end to end through the observability layer
  • Q&A

Your Instructor

  • Richmond Alake

    Richmond Alake is a highly experienced Machine Learning Architect and Engineer with over five years of expertise in the field. He specializes in Computer Vision and Deep Learning and has a proven track record of successfully developing and integrating deep learning models to solve a wide range of problems, such as motion detection, object detection, and pose estimation. Throughout his career, he has worked with a diverse range of clients, including large conglomerates, financial institutions, and small startups. In addition to his professional work, Richmond also serves as an AI advisor to a number of startups in the UK and the US.

    With a background in building websites and mobile applications, Richmond is a firm believer in using technology to solve everyday problems. He has extensive knowledge of Machine Learning and has written over 200 articles on the subject, gaining over a million views. He was recognized as one of Medium's top AI writers in 2020/2021 and has collaborated with companies such as O'Reilly, BuiltIn and Nvidia to develop effective educational and informative learning materials on AI.

    Currently, Richmond Alake is a Machine Learning Architect at Slalom Build UK. As the first hire of the machine learning practice in the UK division, he is responsible for helping organizations move from machine learning research to productionisation and assisting maturing organizations in promoting AI models into existing infrastructure to drive commercial and business value. His main role as an ML Architect is to assist organizations in developing and maintaining machine learning pipelines by implementing MLOps principles, techniques, and tooling. He is well-versed in Feature Stores and has conducted internal training for Data Engineers, Data Scientists, and ML Engineers.

    linkedinXlinksearch

Skill covered

Engineering