7 context engineering techniques for production
Seven context engineering techniques used in production AI systems, with implementation patterns and research on when each one works.
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context as a service
/ˈkɒntekst æz ə ˈsɜːrvɪs/
noun
1.
A managed layer that analyzes your files, extracts concepts and entities, builds a relationship graph, and serves targeted, token-efficient context to any AI agent via MCP.
2.
Shared, living context that agents can both read from and write to. Every piece is tagged to its source, tracked per query, and instantly available to every connected tool and teammate.
Examples
For Teams
"Our product documentation container gives every team member's AI assistant the same context. No more outdated information using Wire."
"We uploaded our style guide, brand assets, and past campaigns. Now our marketing agents all speak with the same voice via Wire."
For Individuals
"My job application container has my resume, portfolio, and case studies. Every AI tool I use knows my background with Wire."
"I uploaded all my research notes and references. Now Claude, Cursor, and my other tools all have the same context using Wire."
"Can't I just use markdown files and custom instructions?" You can. But managing context across tools and teammates becomes a job in itself. Wire extracts concepts and entities from your files, tracks where everything came from, and gives every agent on your team instant access.
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Frequently asked questions
Everything you need to know about Wire.
What is Wire?
Wire creates and manages composable context containers. Add your documents, structured data, or let your agents write entries directly. Wire automatically organizes everything for AI consumption. Your agents access context through MCP. Portable intelligence that works across all your AI tools.
How does it work?
Create a container for your project, team, or use case. Add context through file uploads or let your agents write entries directly. Wire automatically organizes and structures everything into AI-optimized context. Connect from Claude, Cursor, or any MCP-compatible tool.
What file types do you support?
Wire supports common document formats including PDF, Word documents, text files, markdown, CSV, JSON, and more. See the full list of supported file types.
What AI tools work with Wire?
Wire works with any AI tool that supports MCP (Model Context Protocol). This includes Claude Desktop, Claude Code, Cursor, Cline, and more. See setup guides for all supported tools.
What is a composable container?
Each container is private by default and holds your documents and structured data. Wire automatically organizes and structures everything based on what's inside, making your data accessible to agents without manual configuration. Containers adapt to their content.
Can I share containers with my team?
Yes! Wire is built for teams. Invite team members to your organization and everyone can access shared containers. Each team member connects their own AI tools to the same context, so everyone stays on the same page.
Is my data private?
Yes. All containers are private by default and only accessible to you and the team members you explicitly invite. We never share your data with third parties or use it to train AI models.
How does pricing work?
Every new organization gets 3,000 starter credits to try Wire. Pick a plan for monthly credits and the option to buy additional credit packs. All plans include unlimited containers, team members, and MCP access. See our pricing page for details.
From the blog
Context engineering, AI agents, and what we're learning.
Structured Context vs Raw Text for AI
ETH Zurich found AI-generated context files hurt agent performance by 3%. The problem is structure, not volume. Here's what the research says.
Why MCP failures are a context engineering problem
New research analyzed 3,282 MCP bug reports. The patterns reveal a context delivery problem, not a protocol problem. Here's what the research shows.