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Overview

Context engineering is an emerging concept focused on the deliberate design and management of information flow between users, applications, and AI models. Unlike established fields such as prompt engineering, context engineering is still being defined by practitioners as they work to solve the unique challenges of providing AI models with the right information at the right time.
“In 2025, the models out there are extremely intelligent. But even the smartest human won’t be able to do their job effectively without the context of what they’re being asked to do… ‘Context engineering’ is the next level of prompt engineering. It is about doing this automatically in a dynamic system.” — Walden Yan, Cognition AI

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The context journey

Information flows through five stages in a typical MCP system: 
User Input ──► Context Assembly ──► Model Processing
                                          │
                                   Response Generation
                                          │
                                   State Management
                                          │
                              ◄─── Next Interaction
StageDescription
User inputRaw information from the user (text, images, documents)
Context assemblyCombining user input with system context, history, and retrieved information
Model processingThe AI model processes the assembled context
Response generationThe model produces outputs based on the provided context
State managementThe system updates its internal state based on the interaction

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Core areas of context engineering

Context engineering encompasses five areas particularly relevant to MCP:

Context selection

Determining what information is relevant for a given task

Context structuring

Organizing information to maximize model comprehension

Context delivery

Optimizing how and when information is sent to models

Context maintenance

Managing state and evolution of context over time

Context evaluation

Measuring and improving the effectiveness of context

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Emerging principles

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Principle 1: Share context completely

Context should be shared completely between all components rather than fragmented across multiple agents or processes. 
Fragmented approach (problematic):
  Agent 1 ── Context 1
  Agent 2 ── Context 2
  Agent 3 ── Context 3
  (decisions may conflict)

Unified approach (preferred):
  Agent ── Shared Complete Context
  (consistent decisions throughout)
In MCP applications, this suggests designing systems where context flows seamlessly through the entire pipeline.

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Principle 2: Actions carry implicit decisions

Each action a model takes embodies implicit decisions about how to interpret context. When multiple components act on different contexts, these decisions can conflict. Practical implications:
  • Prefer linear processing of complex tasks over parallel execution with fragmented context
  • Ensure all decision points have access to the same contextual information
  • Design systems where later steps can see the full context of earlier decisions

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Principle 3: Balance context depth with window limitations

As conversations grow longer, context windows eventually overflow. Effective context engineering manages the tension between comprehensive context and technical limitations.

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Emerging approaches

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1. Context chunking and prioritization

# Conceptual: Context chunking and prioritization
def process_with_chunked_context(documents, query):
    # 1. Break documents into smaller chunks
    chunks = chunk_documents(documents)

    # 2. Score each chunk for relevance
    scored_chunks = [
        (chunk, calculate_relevance(chunk, query))
        for chunk in chunks
    ]

    # 3. Sort by relevance descending
    sorted_chunks = sorted(scored_chunks, key=lambda x: x[1], reverse=True)

    # 4. Use the top N most relevant chunks
    context = create_context_from_chunks(
        [chunk for chunk, score in sorted_chunks[:5]]
    )

    # 5. Process with prioritized context
    return generate_response(context, query)
This approach works within context window limitations while still leveraging large knowledge bases.

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2. Progressive context loading

User asks question
      │
      ▼
MCP Server sends minimal context to AI Model
      │
      ▼
AI Model produces initial response
      │
      ▼
Does model need more context?
  ├── Yes: Load additional context → re-query model
  └── No:  Return final response to user
Progressive loading starts with minimal context and expands only when necessary — reducing token usage for simple queries while handling complex questions fully.

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3. Context compression and summarization

Full Conversation Context
        │
        ▼
Compression Model
        │
  Compressed Context (essential info only)
        │
        ▼
Main Processing Model ──► Response
Context compression focuses on:
  • Removing redundant information
  • Summarizing lengthy exchanges
  • Extracting key facts and decisions
  • Optimizing for token efficiency

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4. Layered context architecture

Some practitioners find success with context arranged in conceptual layers:
LayerContent
Core layerEssential information the model always needs
Situational layerContext specific to the current interaction
Supporting layerAdditional information that may be helpful
Fallback layerInformation accessed only when needed

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MCP protocol design responses

The MCP protocol was designed with context challenges in mind:
The protocol supports structured, resource-based context that can be referenced efficiently. Resources can be paginated and loaded progressively.
Flexible tooling allows dynamic retrieval of information based on need. Structured prompts enable consistent context organization.
Standardized session management and clearly defined interaction patterns for context evolution.
Protocol design accommodates various content types with standardized representation of multi-modal information.
Clear boundaries between client and server responsibilities, with local processing options to minimize data exposure.

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Multi-agent architecture tradeoffs

While multi-agent architectures are popular, they come with significant context engineering challenges. Consider whether a single-agent approach with comprehensive context management might produce more reliable results for your use case.
ConcernMulti-agentSingle-agent
Context fragmentationHigh riskLow risk
Decision consistencyRequires coordinationNaturally consistent
Communication overheadHighNone
State management complexityHighLower
Debugging difficultyComplexSimpler

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Measuring context effectiveness

Context engineering is still maturing, but several metrics are emerging:

Input efficiency

Context-to-response ratio, token utilization, compression effectiveness

Performance

Latency impact, token economy, retrieval precision

Quality

Response relevance, factual accuracy, consistency, hallucination rate

User experience

Follow-up rate, task completion, satisfaction indicators

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Experimental approach

Context engineering is still in its early stages. Recommended approach:
  1. Establish a baseline with simple context before testing sophisticated methods
  2. Change one thing at a time to isolate the effect of each context change
  3. Combine quantitative metrics with qualitative user feedback
  4. Analyze failures to understand why context strategies fall short
  5. Consider tradeoffs between efficiency, quality, and user experience

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Resources

MCP Documentation

Official MCP specification and implementation guides

Don't Build Multi-Agents

Walden Yan’s insights on context engineering principles

Building Effective Agents

Anthropic’s approach to agent development

Lost in the Middle

Research on how language models use long contexts

Build docs developers (and LLMs) love

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