For knowledge bases that overflow the context window

The agent reaches past its context window

500 pages of documentation. A 200k token window. The agent only ever sees a fraction of what is relevant, and it does not know what it is missing.

When your context outgrows the window

The limits are real

Context overflows the window

500 pages of documentation. 200k token limit. The agent only ever sees a fraction of what is relevant.

Manual curation per session

Every session, you decide which files the agent gets. Different conversations need different context. Exhausting.

Agents answer with partial context

The agent gives partial answers because it only ever sees partial context. It does not know what it does not know.

Whole categories of work blocked

'Search across all our company docs' does not work when the library is 10x larger than the agent's window.

Unlimited context, agent-driven retrieval

A Wire container stores unlimited context. The agent asks for what it needs and gets back just the relevant pieces through MCP. The full library stays available without ever trying to fit it all in the window at once.

  • No practical limit on container size
  • The agent retrieves only the relevant entries
  • Even small context windows reach large knowledge bases
  • Queries that need the whole library become possible

Your knowledge, accessible

Context Window Approach

Your docs 2M tokens
Context window 200k limit
90% left out

Wire Approach

Your docs 2M tokens
Wire container Unlimited
100% accessible via retrieval

Common questions

How much can I store in a Wire container?
Wire containers support a minimum of multiple gigabytes of content. They work best for scoped, well-defined knowledge bases (a product's docs, a team's processes, a project's codebase) rather than dumping everything in one place. Within that scope, Wire handles all the retrieval automatically.
How does Wire decide what context to return?
Wire uses semantic search to find the most relevant entries for each query. The agent receives a targeted, high-relevance excerpt rather than the entire document library.
What context window size do I need to use Wire?
Any size works. Because Wire does the retrieval before the context window, even a small context window can reach a large knowledge base.
Is Wire the same as RAG?
Wire uses retrieval-augmented approaches internally, but it is a managed service. You do not build or maintain a RAG pipeline. Wire handles chunking, embedding, and retrieval automatically.
Can I add more content to a container over time?
Yes. Containers are live. Upload new docs, write new entries, and they are immediately available for retrieval. No re-indexing step required.

Learn more

Dig deeper into context windows and agent retrieval.

Article

Context Rot: Why AI Performance Degrades With More Information

Research shows LLMs drop from 95% to 60% accuracy as context grows stale. Here's how context rot degrades AI performance and why bigger windows won't help.

Article

RAG Is Not Enough: When Retrieval Fails Your AI

RAG is a context-building strategy, not magic. Research shows 70% of retrieved passages miss the mark. Here's why naive retrieval fails and what works.

Article

What Is a Context Window?

A context window is the total text an AI model can process at once. Learn how they work, why size isn't everything, and what actually affects performance.

Scale past context limits

Upload everything. The agent finds what it needs.

Spin Up a Container