[Blog](https://supermemory.ai/blog/) · [Learning](https://supermemory.ai/blog/tag/learning/)

# What Is Context Engineering? A Guide for AI Builders

Choose what an agent sees at each step: instructions, tools, retrieved evidence, memory, and working state. Includes a concrete context-budget example.

By Shardul Mane April 10, 2026 · 4 min read 

![What Is Context Engineering? A Guide for AI Builders](https://supermemory.ai/_astro/cover.CngpVvO3_Z1yzyXP.webp)

Context engineering is the work of deciding what information an AI model receives at each step of a task. It includes instructions, tool definitions, current state, retrieved documents, relevant memories, and selected conversation history. The goal is to supply enough reliable information for the next decision within the model's context and cost constraints.

Prompt engineering focuses on how instructions are written. Context engineering also controls which facts and tools are available, when they arrive, and what happens to them as the task continues.

## Start with the next decision

Consider an assistant preparing a customer renewal summary. It needs the current contract, recent support issues, confirmed customer priorities, and the requested output format. It probably does not need every message ever exchanged with that customer.

Ask what would change the next decision. A current contract amendment may matter more than ten older summaries. A recent support event may be relevant evidence without being an instruction. The distinction affects both retrieval and how the context is labeled.

Anthropic's [context-engineering article](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) describes this as managing the information available during inference. It also discusses selective retrieval and compaction. Those are useful techniques to evaluate, not a fixed architecture every agent must copy.

## Separate information by its role

| Context component   | Example                                   | Treatment                                 |
| ------------------- | ----------------------------------------- | ----------------------------------------- |
| Instructions        | Produce a renewal summary with citations  | Keep clear and internally consistent      |
| Authoritative state | Current contract and account permissions  | Fetch from the owning system              |
| Retrieved evidence  | Support notes and previous commitments    | Keep source identity and dates            |
| Working state       | Sections drafted and unresolved questions | Update as the task progresses             |
| Historical memory   | The customer's preferred reporting style  | Apply only when relevant                  |
| Tool output         | Search results or a database response     | Bound size and preserve needed references |

Do not let text retrieved from a document become a new instruction merely because it appears in the prompt. Likewise, a remembered account role should not substitute for the permission check performed by the application.

## Allocate a budget before filling it

Suppose an application chooses an illustrative 32,000-token operating budget for a request. It reserves 4,000 for output, leaving 28,000 for input. This is an application example, not a claim about a specific model's limits.

One possible input allocation is 3,000 for instructions and tools, 2,000 for the current request, 5,000 for working state, 12,000 for evidence, and 6,000 of headroom. Those values total 28,000\. Unused capacity is acceptable; it is not a quota to fill.

When evidence exceeds its allocation, rank and deduplicate it, retrieve narrower sections, or split the work into steps. Do not truncate blindly through the middle of a citation or remove the only passage supporting an exception. Measure actual tokens with the tokenizer or usage interface for the selected model; word counts are only rough planning aids.

## Build the context at the point of use

A useful request sequence authenticates the caller, loads current task state, retrieves authorized evidence, checks versions, and selects what the next model call needs. The model can request more information when the first evidence set leaves a specific gap.

For example, the renewal assistant might first identify support incidents, then fetch the two relevant incident reports. Loading every report upfront may be wasteful, while allowing unlimited tool exploration may be slow. Record tool calls and evidence use so you can compare those strategies on actual tasks.

There is also a freshness tradeoff. Precomputed summaries are convenient, but current contractual terms should come from the latest approved source. Label summary dates and keep a route back to the original material.

## Preserve state across long tasks

When a task outlasts one context window, carry forward a compact record of the objective, constraints, completed actions, outstanding questions, and evidence references. Keep exact identifiers, numbers, and instructions that would be dangerous to paraphrase incorrectly.

A handoff should say “draft prepared, not sent” when that is the true state. Otherwise, the next session can mistake an intention for a completed external action. This applies to agents continuing their own work as well as work transferred between agents.

The [context-limit guide](https://supermemory.ai/blog/what-happens-ai-agent-runs-out-context-fix/) covers overflow and compaction. The [long-term memory guide](https://supermemory.ai/blog/what-is-long-term-memory-ai/) covers information needed across separate sessions and tasks.

## Evaluate the assembled context, not only the prose

Log which evidence IDs reached the model, how many tokens each component consumed, and which sources the answer cited. If an answer is wrong, this lets you distinguish missing evidence from poor use of available evidence.

Useful tests include a late correction, an older conflicting document, a long irrelevant tool result, an inaccessible record, and a request where no memory is needed. Compare task completion, evidence support, latency, and cost. Smaller context is an improvement only if the task still succeeds.

Start with one workflow and capture ten representative traces. For each trace, identify the information that changed the answer and the information that merely consumed space. That review gives you concrete retrieval and context changes to make before adding another framework or model.

If those traces show that useful context disappears between sessions, [try Supermemory in that workflow](https://console.supermemory.ai/). Start by carrying one confirmed preference or decision into the next conversation, and compare the assembled context with your existing approach.

## Other posts.

1. [How AI memory actually works. A beginner's guide How AI apps remember things between chats, why a bigger context window doesn't solve it, and the parts every memory system shares. EngineeringOct 9, 2026 ](https://supermemory.ai/blog/how-ai-memory-works/)
2. [We're open sourcing the company brain. Here's how we designed the multiplayer harness Company Brain is now open source. A walkthrough of the multiplayer harness behind its Slack experience, from proactivity and memory boundaries to approvals and recovery. NewsSep 25, 2026 ](https://supermemory.ai/blog/open-sourcing-company-brain/)
3. [Jev changes a lot in memory & context engineering. Here's exactly how. We tested Jev across reranking, chunking, observation, and harness decisions. Here is where fast decision models help memory systems, where they cost more, and where they still fall short. EngineeringSep 24, 2026 ](https://supermemory.ai/blog/jev-memory-context-engineering/)
4. [I reverse-engineered Instinct's memory. Here's exactly how it works Instinct keeps its memory as git-tracked markdown files, found with grep rather than vectors. Here is the whole system as far as black-box probing can reconstruct it, and how to rebuild it on supermemory in about 60 lines. EngineeringSep 20, 2026 ](https://supermemory.ai/blog/reverse-engineering-instinct-memory/)
5. [An update to supermemory We've discontinued the supermemory company brain and Nova. Everyone who was charged has been refunded, our MCP and plugins continue to run, and we're going all in on the memory engine. NewsSep 10, 2026 ](https://supermemory.ai/blog/an-update-to-supermemory/)
6. [Scaling Conversations: How Adapta Grew Usage Without Losing Context Adapta added Supermemory as a persistent memory layer so every conversation keeps its context — letting the team scale usage without losing the thread. Case StudyJun 12, 2026 ](https://supermemory.ai/blog/adapta-scaling-conversations/)
7. [How Chatarmin Ditched RAG and Went Memory-Only with Supermemory Chatarmin replaced a heavy RAG pipeline with Supermemory's memory layer — cutting average AI response time from 40s to 12s and token usage by 40–50%. Case StudyJun 10, 2026 ](https://supermemory.ai/blog/chatarmin-memory-only/)
8. [SMFS: making agentic retrieval 55% cheaper AND more accurate We launched SMFS.ai (Supermemory Filesystem) a few weeks ago, with a simple bet: We can redesign the filesystem specifically for agents, with special files, structures, and commands that it can use for it's tasks. Today, SMFS is used by hundreds of companies to power their agents. EngineeringMay 28, 2026 ](https://supermemory.ai/blog/smfs-making-agentic-retrieval-55-cheaper-and-more-accurate/)
9. [Introducing Dynamic Dreaming: supermemory now connects the dots, for you. Dreaming is magical. TLDR: We're launching Dynamic Dreaming in supermemory today, which automatically works if you're using supermemory in any way - API, OpenClaw, Hermes agent, etc. EngineeringMay 25, 2026 ](https://supermemory.ai/blog/introducing-dynamic-dreaming-supermemory-now-connects-the-dots-for-you/)
10. [Dear reader, we just made supermemory insanely cheap... the Context Cloud When I first started building supermemory, I had one goal: To build the best memory system for AI. I would talk to customers, and find out that memory was not the only thing they needed - They were all setting up 7-8 different vendors at the same time. EngineeringMay 18, 2026 ](https://supermemory.ai/blog/dear-reader-we-just-made-supermemory-insanely-cheap-the-context-cloud/)
11. [Introducing @supermemory/tools v2.0.0 Today we're releasing v2.0.0\. This release unifies the API across all agents sdk integrations from AI SDK to Mastra, makes conversation identity a first-class concept, and ships with memory saving on by default. EngineeringApr 27, 2026 ](https://supermemory.ai/blog/introducing-supermemory-tools-v2-0-0/)
12. [Solving the Precision-Recall Tradeoff: Search Result Aggregation When you're building memory for AI, search is your foundational layer. The way search generally works is straightforward: the user defines a query, and then sets a limit (top-K) on how many search results they want returned. Usually, this is set to 10 or 20. EngineeringApr 5, 2026 ](https://supermemory.ai/blog/solving-the-precision-recall-tradeoff-search-result-aggregation/)
13. [OpenClaw Memory Problems: Why It Forgets and How to Fix It (2026) TLDR: Today, we are releasing a new version of our openclaw plugin - https://github.com/supermemoryai/openclaw-supermemory. This post is going to be a bit technical, so bear with me (or bookmark for later!) In this post, I will talk about what we do about OpenClaw memory, and how we fix it. EngineeringFeb 19, 2026 ](https://supermemory.ai/blog/why-everyone-is-complaining-about-openclaws-memory-it-sucks-and-why-supermemory-fixes-it/)
14. [Stateful Coding Agents with Memory: Build Long-Running Agents (2026) We built a plugin for Claude Code and OpenCode that gives your coding agent persistent memory. It remembers your preferences, learns your codebase, and never loses context mid-conversation. The result is an agent you can run for months without starting over. EngineeringFeb 18, 2026 ](https://supermemory.ai/blog/infinitely-running-stateful-coding-agents/)
15. [Clawd / Molt bot's memory SUCKS. We gave it supermemory. I'm the founder of supermemory. Clawd/Molt bot is blowing up right now, with many, many use cases. I set it up, too, and have been using it through telegram. TLDR: just go to https://supermemory.ai/docs/integrations/clawdbot to set up supermemory for your clawd bot. EngineeringJan 28, 2026 ](https://supermemory.ai/blog/clawd-molt-bots-memory-sucks-we-gave-it-supermemory/)
16. [Catch up with our UNFORGETTABLE Launch Week Over the last year, one belief has guided almost everything we’ve built at Supermemory AI becomes meaningfully useful only when it remembers. Memory shouldn’t be something developers rebuild from scratch. It shouldn’t be fragile, expensive, or trapped inside a single tool. NewsJan 4, 2026 ](https://supermemory.ai/blog/catch-up-with-our-unforgettable-launch-week/)
17. [Empowering the Next Generation of Founders: Supermemory Startup Program If there’s one thing we’ve learned while building Supermemory, it’s that most startups don’t fail because they didn't build features; they fail when infrastructure slows them down, or they built too slow. NewsDec 31, 2025 ](https://supermemory.ai/blog/empowering-the-next-generation-of-founders-supermemory-startup-program/)
18. [Building code-chunk: AST Aware Code Chunking At Supermemory, we're building context engineering infrastructure for AI. A huge part of that is dealing with code: ingesting repos, understanding structure, and making it searchable. The problem is that most code chunking solutions are terrible. We built code-chunk to fix this. EngineeringDec 29, 2025 ](https://supermemory.ai/blog/building-code-chunk-ast-aware-code-chunking/)
19. [Supermemory raises $3 million with the best memory engine for LLMs Today, I am excited to announce our first funding round to accelerate our mission of building an interoperable, scalable and reliable memory for LLMs and agents. Memory is one of the hardest challenges in AI right now. NewsOct 6, 2025 ](https://supermemory.ai/blog/supermemory-raises-3-million-and-building-the-best-memory-engine-for-llms/)
20. [Mem0 vs Supermemory: Why Scira Switched Scira AI moved its production memory layer from Mem0 to Supermemory. This is what failed, what improved, and how the team evaluated the two systems. Case StudyOct 2, 2025 ](https://supermemory.ai/blog/why-scira-ai-switched/)
21. [Never Record Again: How Montra Uses Supermemory to Rethink Video Creation Campbell Baron, the founder of Montra, has been making videos since he was twelve. By thirteen, he was already doing brand work. Today, he’s betting on a very different future for creators: a world where recording is the exception, and most videos are generated from scratch. Case StudyAug 21, 2025 ](https://supermemory.ai/blog/never-record-again-how-montra-uses-supermemory-to-rethink-video-creation/)
22. [Unified Memory That Works Where You Work: Your Second Brain With Supermemory Hi everyone, I’m Dhravya, the founder of Supermemory. I want to start with a little story behind why this product means so much to me. You can also skip straight to what it is and how it works below. EngineeringJul 25, 2025 ](https://supermemory.ai/blog/unified-memory-that-works-where-you-work-your-second-brain-with-supermemory/)
23. [Supermemory just got faster on PlanetScale What is Supermemory? Supermemory completes the missing part of the LLM puzzle: memory. Just as memory is crucial for human intelligence, it's essential for truly intelligent AI systems. EngineeringJul 18, 2025 ](https://supermemory.ai/blog/supermemory-just-got-faster-on-planetscale/)
24. [Faster, smarter, reliable infinite chat: Supermemory IS context engineering. People are obsessed with prompts and prompt engineering. Sure, what you say is important, but what the model knows when you say it is the difference between a stateless text generator and an intelligent AI system. In short, context is the most crucial component. NewsJul 9, 2025 ](https://supermemory.ai/blog/faster-smarter-reliable-infinite-chat-supermemory-is-context-engineering/)
25. [We solved AI API interoperability One API to rule them all, One spec to find them, One library to bring them all and in the TypeScript, bind them. When we were building the Infinite Chat API, initially, we only supported the OpenAI format. This was fine, until a lot of our customers started asking for more. EngineeringJul 7, 2025 ](https://supermemory.ai/blog/we-solved-ai-api-interoperability/)
26. [The Wow Factor of Memory - How Flow Used Supermemory To Build Smarter, Stickier Products Overview: Flow is a note-taking app built around a bold vision: to create a more personal, context-aware writing experience powered by AI. At the heart of this mission is memory. Case StudyJun 14, 2025 ](https://supermemory.ai/blog/the-wow-factor-of-memory-how-flow-used-supermemory-to-build-smarter-stickier-products/)
27. [The UX and technicalities of awesome MCPs Last month, we launched the Supermemory MCP, mostly to test our own infrastructure and get some initial traction. It blew up. To my absolute surprise, the initial launch itself got half a million impressions (!!!). Then, we launched and got #2 on ProductHunt too. EngineeringJun 8, 2025 ](https://supermemory.ai/blog/the-ux-and-technicalities-of-awesome-mcps/)
28. [Architecting a memory engine inspired by the human brain Language is at the heart of intelligence, but what truly powers meaningful interaction is memory — the ability to accumulate, recall, and contextualize information over time. Large Language Models (LLMs) have mastered language, but memory remains their Achilles’ heel. EngineeringJun 5, 2025 ](https://supermemory.ai/blog/memory-engine/)

## Start building with supermemory.

Memory and continual learning for any model, any harness. Available through our API, plugins, and MCP.

[Build with supermemory ](https://console.supermemory.ai/)