What is Context Engineering?
Context Engineering selects and organizes the information available to a model at decision time. The aim is not the most information, but the most relevant information for the task.
Context can include instructions, user requests, retrieved documents, tool results, conversation state, memory and current task state.
Why is a prompt alone not enough?
A prompt matters, but enterprise work also depends on current data, user role and workflow state. These facts sit outside prompt text.
A precise prompt cannot substitute for a trusted source, correct permission or actual process state.
What makes up Context?
The components must be selected for the task. Irrelevant material can reduce quality, speed and traceability.
- System Instruction
- User Request
- Retrieved Documents
- Tool Results
- Conversation State and Memory
- Metadata and Permissions
- Current Task State
The role of RAG and Memory
RAG retrieves relevant documents when needed. Memory can preserve useful state or preference, but it should not replace a trusted current source.
Both need versioning, ownership and permission controls.
Context in AI agents
An agent decides at each step what to retain, which tool to call and which information to discard. Context Engineering is therefore part of agent workflow design.
MCP can provide standardized tool and Context access, but choosing and governing the right data remains an architectural responsibility.
Too much Context is also a problem
Very large or irrelevant Context can increase cost and latency while hiding important signals. Conflicting information can make model behavior unstable.
Controlled summarization, targeted retrieval, metadata and expiry handling help preserve high-signal Context.
Security and access
Context should be permission-aware: the same requesting identity and access level must limit retrieval and tools.
Sensitive data, secrets and untrusted instructions should not enter Context without boundaries and validation.
- Permission-aware retrieval
- Source provenance
- Secret isolation
- Logging of data and tool use
Context Engineering in the enterprise
An organizational answer may need a valid contract, workflow state, user role and a tool result. Good design makes it useful and traceable.
Context Engineering at Aivan
At Aivan, Context should come from permitted sources, current data and real process state. Each scenario begins by defining the question, source, access, approved tools and evaluation criteria.
Frequently asked questions
What is Context Engineering?
The practice of composing and maintaining the Context that lets a model or agent behave as intended.
Is it the same as Prompt Engineering?
No. A prompt is one component; Context Engineering manages the full state available to a model.
Is more Context always better?
No. Irrelevant, stale or very large Context can raise cost, latency and error.
What does RAG do?
RAG retrieves relevant organizational Context and should be paired with permissions and metadata.
Sources and further reading
- AnthropicEffective context engineering for AI agents29 September 2025 · Official engineering article
- AnthropicBuilding effective AI agents19 December 2024 · Official engineering article
- Model Context ProtocolWhat is the Model Context Protocol (MCP)?Official documentation