The AI landscape has witnessed a significant evolution from the early days of “prompt engineering” to what we now recognize as “context engineering.” While prompt engineering captured our imagination in the GPT-3 era with clever tricks and wordsmithing, context engineering represents a more systematic and scalable approach to building production-ready AI systems.
What is Context Engineering?
Context engineering is the art of providing all the context for the task to be plausibly solvable by the LLM, as described by Tobi Lutke. More precisely, context engineering is the discipline of building dynamic systems that supply an LLM with everything it needs to accomplish a task.
Unlike prompt engineering, which focuses on crafting individual instructions, context engineering takes a systems-level approach. It encompasses the entire information ecosystem that surrounds an AI model—including memory, retrieval systems, tool integrations, and the dynamic flow of information across multiple interactions. This systems thinking becomes critical when deciding between autonomous vs controlled agent architectures, where context management determines agent behavior and reliability.
The Fundamental Difference: Prompt vs Context Engineering
The distinction between these two approaches runs deeper than many realize:
Scope and Purpose
Prompt Engineering focuses on what to say to the model at a moment in time. Context Engineering focuses on what the model knows when you say it — and why it should care.
Prompt Engineering:
- Operates within a single input-output pair
- Focuses on crafting clear, specific instructions
- Relies heavily on wordsmithing to get things “just right”
- Great for one-off tasks and creative applications
- Can be done with nothing but a chat interface
Context Engineering:
- Manages the entire context window across sessions
- Designs systems for consistent performance at scale
- Focuses on delivering the right inputs at the right time
- Built for long-running workflows and complex state management
- Requires memory modules, RAG systems, and API coordination
Relationship and Hierarchy
Prompt Engineering is a subset of Context Engineering, not the other way around. Think of it this way: Prompting tells the model how to think, but context engineering gives the model the training and tools to get the job done.
Context engineering encompasses prompt engineering as one component within a larger architectural framework. Prompt engineering focuses on writing instructions for single tasks, while context engineering designs systems that manage information flow across multiple interactions. This hierarchy becomes evident when scaling from single agent to multi-agent systems, where individual prompt design must consider the broader context of agent coordination and collaboration.
Core Principles of Context Engineering
Based on real-world experience from building production AI agents, several key principles have emerged:
1. Design Around the KV-Cache
The KV-cache hit rate is the single most important metric for a production-stage AI agent. It directly affects both latency and cost. This principle drives several practical design decisions:
- Keep prompt prefixes stable: Even single-token differences can invalidate the cache
- Make context append-only: Avoid modifying previous actions or observations
- Use deterministic serialization: Ensure consistent JSON key ordering
- Mark cache breakpoints explicitly: Account for cache expiration in your architecture
The economic impact is substantial—with Claude Sonnet, cached tokens cost $0.30/MTok while uncached ones cost $3/MTok, representing a 10x difference. These efficiency gains become crucial when building agentic AI systems that revolutionize software development economics, where context management costs directly impact project viability.
2. Mask, Don’t Remove
As AI agents gain more capabilities, their action spaces naturally expand. Rather than removing tools, it masks the token logits during decoding to prevent (or enforce) the selection of certain actions based on the current context.
This approach solves two critical problems:
- Maintains KV-cache validity by keeping tool definitions stable
- Prevents model confusion when previous actions reference tools no longer in context
3. Use the File System as Context
Modern LLMs offer large context windows, but in real-world agentic scenarios, that’s often not enough, and sometimes even a liability. The solution is treating the file system as ultimate context:
- Unlimited in size and persistent by nature
- Directly operable by the agent itself
- Enables compression strategies that are always restorable
- Allows models to externalize long-term state instead of holding it in context
This approach aligns directly with the Model Context Protocol (MCP) foundation for truly agentic AI, where structured context exchange through MCP enables agents to efficiently manage and share information beyond their immediate context windows.
4. Manipulate Attention Through Recitation
By constantly rewriting the todo list, Manus is reciting its objectives into the end of the context. This pushes the global plan into the model’s recent attention span, avoiding “lost-in-the-middle” issues and reducing goal misalignment.
This technique effectively uses natural language to bias the model’s focus toward task objectives without requiring architectural changes.
5. Keep the Wrong Stuff In
Counter-intuitively, one of the most effective techniques is preserving failure traces: When the model sees a failed action—and the resulting observation or stack trace—it implicitly updates its internal beliefs. This shifts its prior away from similar actions, reducing the chance of repeating the same mistake.
Error recovery has emerged as one of the clearest indicators of true agentic behavior, yet it remains underrepresented in academic benchmarks.
6. Don’t Get Few-Shotted
While few-shot prompting is valuable for prompt engineering, it can backfire in agent systems. Language models are excellent mimics; they imitate the pattern of behavior in the context. If your context is full of similar past action-observation pairs, the model will tend to follow that pattern, even when it’s no longer optimal.
The solution is introducing structured variation—different serialization templates, alternate phrasing, and controlled randomness to break patterns.
Practical Implementation Strategies
Memory Management
Context engineering requires sophisticated memory management strategies:
- Hierarchical memory: System prompt, session memory, and working memory
- Selective compression: Preserve essential context while compressing verbose observations
- Memory indexing: Enable efficient retrieval of relevant past interactions
Tool Integration
Unlike simple prompt-based systems, context-engineered agents require:
- Dynamic tool loading: Runtime discovery and integration of capabilities
- Tool state management: Tracking tool availability and constraints
- Result contextualization: Integrating tool outputs meaningfully into ongoing context
Workflow Orchestration
Context engineering extends beyond individual model calls to orchestrate entire workflows:
- State machines: Managing agent behavior across different contexts
- Context transitions: Seamless handoffs between different operational modes
- Error recovery: Graceful handling of failures while preserving context
These orchestration patterns become especially powerful when combined with the emerging B2A SaaS model, where context-engineered agents can interact with multiple specialized services seamlessly.
The Future of AI System Design
Context engineering is still an emerging science—but for agent systems, it’s already essential. Models may be getting stronger, faster, and cheaper, but no amount of raw capability replaces the need for memory, environment, and feedback.
As we move toward more sophisticated AI agents, context engineering will become the primary discipline for AI system architects. Context engineering transforms AI agents from basic chatbots into powerful, purpose-driven systems.
The implications extend beyond technical implementation to fundamental questions about AI system design:
- How do we build AI systems that maintain coherent behavior across extended interactions?
- What architectural patterns enable reliable scaling from prototype to production?
- How do we balance model capabilities with system constraints and requirements?
Conclusion
The evolution from prompt engineering to context engineering reflects the maturation of AI systems from experimental tools to production infrastructure. While prompt engineering remains valuable for specific applications, context engineering provides the systematic framework necessary for building reliable, scalable AI agents.
Prompt engineering gets you the first good output. Context engineering makes sure the 1,000th output is still good. As organizations increasingly depend on AI agents for critical workflows, understanding and implementing context engineering principles becomes essential for long-term success.
The future belongs to AI systems that can maintain coherent behavior across complex, multi-step interactions. Context engineering provides the conceptual framework and practical techniques to build these systems effectively. The agentic future will be built one context at a time. Engineer them well.
For a deeper dive into context engineering practices from a production perspective, I highly recommend reading the detailed case study from the Manus team: Context Engineering for AI Agents: Lessons from Building Manus.
Related Reads:
- Thinking in Agents: The Future of Software Design - How context-driven design reshapes our approach to building AI systems
- Building Multi-Agent Research Systems - Practical examples of context engineering in multi-agent scenarios
- Evolving UX Patterns for Agentic Applications - How context management affects user experience in AI applications