Context engineering
Context engineering is the discipline of designing, sourcing, and delivering the right information to an AI model at the right time. Learn how to build reliable, field-specific context pipelines and how Akyn helps you operationalize them via MCP.
Context engineering is the practice of systematically providing an AI model with the right context—accurate, scoped, and timely—so it can reason, plan, and act reliably. As AI agents move from demos to production, prompt tweaks alone stop working. What separates a helpful agent from a fragile one is the quality of the context pipeline behind it: what you retrieve, how you structure it, and how you control it over time.
Why “context” is the real product
Large language models are generalists. In real workflows, however, agents need field-specific truth: internal policies, up-to-date product docs, runbooks, compliance checklists, pricing rules, or domain research. Without that grounding, agents hallucinate, produce outdated answers, or take incorrect actions. Context engineering turns scattered knowledge into an operational input to your agent—measurable, testable, and improvable.
The core building blocks of context engineering
1) Source quality and scope
Start by defining a crisp scope: what questions your agent should answer and what it should refuse. Then curate sources that are authoritative and maintainable. The best context is not “more text,” but less ambiguity.
2) Normalization and chunking
Raw documents are rarely AI-ready. Context engineering includes cleaning, deduplicating, and structuring content so retrieval returns coherent units (for example: a single policy clause, a step-by-step procedure, or a configuration reference) instead of long, noisy blobs.
3) Retrieval strategy
Retrieval-augmented generation (RAG) is the common mechanism, but context engineering goes further: selecting hybrid search, reranking, filters (tags, recency, source type), and strict citation or quoting rules for high-stakes domains.
4) Assembly and formatting
Even perfect snippets can fail if assembled poorly. Order, headings, and explicit constraints matter. A practical pattern is: (a) task, (b) constraints, (c) retrieved facts, (d) required output format. This reduces model confusion and makes behavior more consistent.
5) Evaluation and iteration
Treat context like code. Add test questions, track failure modes (missing context, conflicting sources, outdated answers), and re-ingest or rewrite sources. The feedback loop is where reliability is won.
Common pitfalls (and how to avoid them)
- Overstuffing the window: too much context increases latency and reduces answer quality. Prefer smaller, higher-signal chunks.
- Stale knowledge: context needs re-ingestion and versioning. Outdated docs are worse than no docs.
- No access boundaries: sensitive context must be gated with access control and auditable usage.
- Unclear provenance: require sources and keep a link back to the original document so humans can verify.
Operationalizing context engineering with Akyn
Akyn is built to make context engineering practical for agent builders and domain experts. You can ingest content from URLs and common document formats (PDF, Markdown, DOCX, text), and Akyn normalizes it for retrieval. Updates trigger automatic vector embeddings so your knowledge stays searchable and RAG-ready without extra plumbing. Knowledge bases can be public or private, with flexible access models.
Most importantly, Akyn delivers your curated context where builders work through MCP (Model Context Protocol), enabling compatible clients and agent frameworks to query field-specific knowledge on demand. If you’re building agents that must be correct, repeatable, and domain-aware, context engineering is the lever—and Akyn is the infrastructure to ship it.
To learn more about connecting knowledge bases to your agents, visit the Akyn documentation.