Context Engineering: Managing Your AI's Attention Budget
How to build high-performance AI systems by managing context like a finite resource
The playbook for building high-performance, cost-efficient AI has evolved. Context engineering is replacing prompt engineering as the core discipline. Here’s what you need to know.
Anthropic just published a deep dive on this shift, and it’s reshaping how I build AI systems and what I teach in my courses: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
What It Is
Context engineering is the strategic curation and management of tokens (information) during LLM inference. It’s not just about crafting the perfect question anymore. It’s about answering: “What configuration of context is most likely to generate the desired behavior?”
Think of it like this: if the LLM is the CPU, the context window is the RAM. Context engineering is your operating system for managing that finite but critical resource.
Why Your Attention Budget Matters
Every token you feed an AI agent consumes part of its limited “attention budget.” AI agents now handle complex, multi-step tasks that accumulate massive amounts of information. Poor context management leads to:
Exceeding context window limits
Degraded performance and hallucinations
Skyrocketing costs and latency
Lost critical information between sessions
The constraint is real. The question is: how do you spend your tokens wisely?
The Shift: “Just in Time” Context Loading
Traditional approaches pre-load all relevant data upfront. The new paradigm mirrors human cognition: maintain lightweight identifiers (file paths, queries, web links) and dynamically load data at runtime using tools.
I’ve been using Claude Code extensively for prompt optimization and building subagents, and this is where context engineering becomes immediately practical. The model generates targeted queries, stores results, and utilizes bash commands to analyze large datasets without ever loading full objects into memory. It’s incredibly efficient.
How to Practice It
Four core strategies I’m applying in Claude Code:
Write - Save information outside the context window (memory systems, scratchpads)
Select - Dynamically fetch only what’s needed at runtime
Compress - Summarize and truncate stale information
Isolate - Use sub-agents with separate context windows for complex workflows
The Bottom Line
Context engineering is becoming what data preprocessing became for machine learning: not optional if you’re serious about production AI.
As models get smarter, they’ll need less prescriptive engineering. But treating context (and attention budget) as a precious, finite resource will remain central to building reliable agents.
My take: If you’re building with Claude Code or managing complex agentic workflows, start thinking in attention budgets. Every token is a resource allocation decision.
Read the full Anthropic article for implementation details and advanced strategies: https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
What strategies are you using to optimize your AI’s context?
P.S. We cover context engineering and hands-on agent building in my “Hands-on AI for Leaders” course. Starts October 6th. Enroll here: https://maven.com/james-gray/hands-on-ai-for-leaders