Context and Harness Engineering for Agentic Artificial Intelligence (AI): Designing Reliable, Context-Aware AI Agents
About This Course
As organisations move from chatbots to autonomous Artificial Intelligence (AI) agents, prompt engineering alone is no longer sufficient. Reliable AI agents now depend on context engineering—a discipline that has rapidly become central to applied AI—and on the broader practice of engineering the agent's harness: the scaffolding of tools, memory, state, and control logic that surrounds the model. Because the harness contains and delivers the context, the two are nested rather than parallel disciplines.
This Agentic AI course equips professionals to build reliable, context-aware AI agents by mastering these two disciplines. As agents operate over many turns and longer time horizons, performance depends not only on model capability, but on what enters the model's finite context window and on the scaffolding wrapped around the model—its harness of tools, memory, state, and control logic.
Using hands-on exercises and light Python programming, participants learn to curate high-signal context (system instructions, tools exposed via function calling and the Model Context Protocol (MCP), examples, and retrieved knowledge), apply retrieval strategies—including traditional Retrieval-Augmented Generation (RAG) as well as just-in-time and agentic retrieval—and engineer a harness that sustains coherence across long tasks using compaction, structured memory, and sub-agent patterns. The course concludes with trace-based evaluation, guardrails (including defences against prompt injection and other agent-security risks), and responsible deployment for customer and user experience contexts.
At the end of the course, participants will be able to diagnose why agents fail, engineer the context and harness that make them dependable, and evaluate agent reliability before deployment, turning promising prototypes into production-ready agentic systems.
What You'll Learn
- Distinguish context engineering, and the broader harness engineering that contains it, from prompt engineering, and evaluate how context limitations, attention budgets, and context degradation ("context rot") impact the reliability of agentic Artificial Intelligence (AI) systems
- Engineer the core components of an agent's context, system instructions, tools, examples, retrieved knowledge, and message history, to maximise signal within the context window
- Apply retrieval strategies—including traditional Retrieval-Augmented Generation (RAG) as well as just-in-time and agentic retrieval, to dynamically provide AI agents with relevant context during execution
- Build and configure an AI agent harness around a model using light Python programming, covering tool contracts (tool definitions and schemas), memory management, state persistence, and control or guardrail logic
- Build long-horizon agentic AI workflows using context compaction, structured memory, note-taking systems, and sub-agent architectures to maintain performance beyond context window limitations
- Evaluate AI agent reliability using trace-based evaluation, and design guardrails and governance —including protection against prompt injection and other agent-security risks—appropriate for customer and user experience deployment
Entry Requirements
As this programme is conducted in English, participants should be proficient in reading, writing, and communicating in English.