Context engineering: what separates a demo from a product
Design and operation of the context that feeds the models: system prompts, available tools, memory, RAG and usage policies. The layer that impacts quality the most - and that almost nobody looks after.
The problem we solve.
When an AI agent fails in production, the problem is rarely the model. Almost always it's the context it receives: a badly written system prompt, a poorly described tool, a RAG fragment that confuses more than it helps, or memory that pollutes new conversations with old data.
Context engineering is the (relatively new) discipline that designs, tests and maintains that layer with the same rigour applied to designing an API. Without it, agents degrade within weeks in production.
What is included
- Design and iterative refinement of system prompts
- Tool catalogue with descriptions, schemas and examples
- Memory strategy: what to remember, what to forget, how to clean up
- RAG with chunking, retrieval and reranking adapted to the use case
- Automated eval suite to detect context regressions
Our process for Context engineering.
Audit
Analysis of the current context with quality metrics: what % of failures are down to context rather than the model?
Redesign
We restructure prompts, tools and RAG strategy with A/B tests over quality evals.
Operation
Continuous evaluation pipeline and a process to iterate the context as the product changes.
Why this service exists on its own
Many teams build their AI agent, launch it, and discover quality keeps declining over time. What fails isn't the model (it's the same one); it's that the context keeps going stale: new tools poorly documented, memory growing unchecked, RAG documents that no longer apply. This service exists for those cases - agents that already work but are degrading because nobody is actively looking after the context layer.
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