Token Limits Aren't Your Problem. Context Engineering Is.
Everyone's obsessing over token limits.
They're solving the wrong problem.
Google just dropped a guide on multi-agent context management that exposes what most developers get catastrophically wrong: treating context like string concatenation when it's actually an architecture problem.
Read it. Several times.
If you're building with AI—or plan to—this matters.
The Default Is Broken
Here's what 90% of developers do: append everything into one giant prompt.
Or use RAG to get context into a single chat turn. That’s fine for chat apps.
But that’s AI’s past use case. Not the future.
And prompt engineering is in the past, too.
The old way:
More chat history? Concatenate.
More tool outputs? Dump it in.
15MB CSV file? Shove it straight into the context window.
This creates three killers:
Cost and latency spiral from growing context size, performance collapse from the "lost in the middle" effect, and hallucination spikes when agents misattribute who did what across a multi-agent system.
Google's research confirms it: raw context dumps aren't just inefficient—they actively degrade performance.
Context as Compiled View, Not String Buffer
The breakthrough: context is a compiled view over a richer stateful system.
Not a mutable string you keep appending to.
Google's ADK framework uses a four-layer architecture:
Working Context – what actually gets sent to the LLM for this specific call
Session – durable log of messages, tool calls, control signals
Memory – searchable long-term knowledge that survives sessions
Artifacts – large files stored as references, not inline blobs
Two additional important engineering angles:
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They’re all strongly typed, and
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Instead of dumping everything, you build transformation pipelines.
Agents receive the minimum required context by default and explicitly request additional information via tools.
Think: you wouldn't load your entire database into RAM for every function. Why do it with context?
What Belongs in the Window Right Now
Once a structure exists, the challenge shifts to relevance.
ADK answers this through collaboration between human domain knowledge and agentic decision-making. Engineers define where data lives and how it's summarized. Agents decide dynamically when to "reach" for specific blocks.
For large payloads, ADK uses the "handle pattern." A 15MB CSV lives in artifact storage, not the prompt. Agents see only lightweight references by default. When raw data is needed, they explicitly load it. Once done, it offloads.
Permanent tax becomes on-demand access.
For caching, the architecture splits context into stable prefixes (system instructions, identity, summaries) and variable suffixes (latest turns, tool outputs). Stable parts get cached. Costs drop.
Multi-Agent Doesn't Scale Without This
Single-agent systems bloat. Multi-agent amplifies it exponentially.
The anti-pattern: letting sub-agents inherit full conversation history from parent agents. This causes context explosion and hallucination when agents misattribute the broader system's history to themselves.
The fix: conversation translation. Prior Assistant messages convert to narrative context with attribution tags. Each agent assumes its role without stealing credit for other agents' work.
Here are several other research-driven blogs on this:
https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
https://blog.langchain.com/context-engineering-for-agents/
https://natesnewsletter.substack.com/p/i-read-everything-google-anthropic
https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus
The Mentors & Minions Connection
With agentic AI, every developer gets:
Mentors who explain concepts repeatedly
Minions who handle grunt work
But if you're managing context poorly, both fail.
Does your AI mentor re-read your entire project for every question? Slower. More expensive. Your minion agents carrying baggage from unrelated tasks? They hallucinate.
Good context engineering means your AI team works for you instead of around architectural debt you didn't know existed.
Why This Matters Now
Most tutorials teach prompt stuffing because it's easy to demo.
You hit limits. Think "I need a bigger model." Real issue? Architecture.
This isn't efficiency optimization. It's understanding how these systems work.
Because “knowing how to code” is not a valuable skill. That's a commodity these days.
The future of engineering is the future of managing AI models - as developer tools and as product functionality providers.
The gap widens daily between developers treating AI as a battle of LLMs versus those architecting around the constraints of all models.
In five years, the difference between these two types of devs will be stark.
You don't need Google's ADK to apply these insights. The patterns work universally:
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Separate storage from presentation
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Cache stable prefixes, update variable suffixes
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Use handles for large payloads
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Translate context during agent handoffs
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Let agents request what they need instead of getting everything
The developers who understand this now are building on rock.
Everyone else is building on sand.
Bookmark the blog. Learn to think from first principles rather than being “taught”.
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