Build Agents with Agent Context in 2026: Free Hands-On Course for ML Practitioners

Build Agents with Agent Context in 2026: A Free Hands-On Course for ML Practitioners

A groundbreaking free course, The Context Course, has launched in 2026 to help machine learning practitioners master building agents with agent context. Designed for developers, researchers, and AI engineers, this program teaches how to define, optimize, and deploy context-aware AI systems using SKILLS.md, subagents, plugins, MCP, and hooks—without bloating prompts or sacrificing performance.

What Is Agent Context—and Why It Matters in 2026

Agent context refers to the dynamic, task-specific information an AI agent uses to make decisions, retain memory, and delegate responsibilities. Unlike traditional LLMs that treat every prompt as isolated, context-aware agents maintain state across interactions. This reduces hallucinations, improves accuracy, and enables complex multi-step workflows. In 2026, context management is now as critical as model size.

How SKILLS.md Defines Agent Capabilities

SKILLS.md is a declarative file format that lists an agent’s tools, triggers, and constraints in human-readable YAML or Markdown. Instead of hardcoding prompts, developers define what an agent can do, when to use it, and how to pass context. For example, a SKILLS.md file might specify: "Use Code Interpreter when user asks to run Python code," ensuring seamless tool integration without prompt drift.

Subagents: Modular Intelligence for Scalable Systems

Anthropic’s subagent architecture enables primary agents to offload specialized tasks—like code generation, data retrieval, or summarization—to smaller, focused subagents. This keeps main conversations clean and reduces context overload. Subagents can be chained, nested, or activated conditionally based on user intent, creating adaptive, hierarchical AI workflows that scale efficiently.

Plugins, Hooks, and MCP: Building Interoperable Agent Networks

Plugins allow external tools—databases, APIs, code interpreters—to be securely connected to agents. Hooks trigger context-aware responses based on events like keyword detection or memory thresholds. MCP (Model Context Protocol) standardizes how context is passed between agents, ensuring consistency across environments. Together, these components form the backbone of modern LLM orchestration.

Real-World Projects: Build, Test, and Deploy

Participants in The Context Course build real agent networks using models like Claude, Codex, and Pi. Projects include: automating customer support with memory-aware subagents, creating code-review agents using SKILLS.md, and deploying context-filtered dataset pipelines. Weekly live AMAs, interactive quizzes, and peer reviews ensure hands-on mastery.

The course emphasizes context hygiene: preventing redundant memory use, avoiding prompt drift, and aligning subagents with core goals. With no cost to enroll, The Context Course removes barriers to advanced AI engineering. Early participants report 40%+ improvements in agent reliability and task completion rates.

As AI evolves, building agents with agent context is no longer optional—it’s essential. Master the frameworks powering the next generation of AI: SKILLS.md, subagents, plugins, and context-aware orchestration. Enroll today on HF-Learn and lead the shift toward intelligent, memory-driven systems.