Effective context engineering for AI agents
by Anthropic
Curate the finite token budget that decides how well your agent behaves.
Overview
In 'Effective context engineering for AI agents,' Anthropic's Applied AI team frames context as a critical but finite resource and positions context engineering as the natural evolution of prompt engineering: the set of strategies for curating and maintaining the optimal set of tokens during LLM inference, including everything that lands in context beyond the prompt. The post distinguishes context engineering from prompt engineering, explains why it is central to building capable agents, and details the anatomy of effective context, context retrieval and agentic search, and techniques for long-horizon tasks—compaction, structured note-taking, and sub-agent architectures. Rather than 'finding the right words,' it reframes the problem as 'what configuration of context is most likely to generate the model's desired behavior?'
At a Glance
- Topic
- Skills
- Level
- Advanced
- Format
- Guide
- Cost
- Free
- Duration
- ~20 min read
- Provider
- Anthropic
- Hands-on
- No
- Certificate
- None
What You’ll Learn
- ✓Why context (not just the prompt) governs agent behavior
- ✓How context engineering differs from and extends prompt engineering
- ✓The anatomy of effective context and agentic retrieval/search
- ✓Techniques for long-horizon tasks: compaction, structured note-taking, sub-agents
- ✓How to reason about a finite token budget as a design constraint
Highlights
- •Authoritative treatment from Anthropic's Applied AI team
- •Concrete long-horizon patterns (compaction, note-taking, sub-agents)
- •Released alongside Claude Sonnet 4.5
Who It’s For
Best For
- ✓Engineers building multi-step or long-running agents
- ✓Anyone hitting context-window and reliability limits
Prerequisites
- •Understanding of LLM prompting basics
- •Some experience building agents or RAG systems
FAQ
What is Effective context engineering for AI agents?
Anthropic's engineering guide (Sept 29, 2025) on context engineering — curating and managing the tokens that power an agent across long-horizon tasks. For engineers building agents where the right context, not just the right prompt, drives reliability.
Is Effective context engineering for AI agents free?
Effective context engineering for AI agents is free to access.
What level is Effective context engineering for AI agents for?
Effective context engineering for AI agents is aimed at a advanced audience. Recommended background: Understanding of LLM prompting basics, Some experience building agents or RAG systems.
How long does Effective context engineering for AI agents take?
Expect roughly ~20 min read. Most learners work through it at their own pace.
What will I learn from Effective context engineering for AI agents?
You'll learn: Why context (not just the prompt) governs agent behavior; How context engineering differs from and extends prompt engineering; The anatomy of effective context and agentic retrieval/search; Techniques for long-horizon tasks: compaction, structured note-taking, sub-agents; How to reason about a finite token budget as a design constraint.