Context Engineering: the Power Behind AI Agents

As AI agents mature from novelty chatbots to autonomous collaborators capable of reasoning, planning, and decision-making, the focus moving from “prompt engineering” which focuses on getting the inputs right to the more powerful paradigm of ‘context engineering’ which is becoming the backbone of reliable, adaptive, and enterprise-ready AI agents.

What exactly is ‘Context Engineering’?

Context engineering is the science and practice of delivering the right information, in the right format, at the right time thereby enabling AI agents to act intelligently and consistently across tasks. While prompt engineering involves crafting a single instruction, context engineering designs the entire informational ecosystem in which the agent operates. This includes:

• Knowledge: Factual data, user history, prior interactions.

• Tools: APIs, MCP servers, function libraries.

• Memory: Short-term (e.g., chat history), long-term (e.g., user preferences, summaries).

• Structure: Schemas, JSON templates, conversational state.

• Workflow State: Where the agent is in the process and what needs to happen next.

Why is Context Engineering critical?

As more and more agents are tackling real-world situations the true differentiator is context quality as it has profound implications:

• Improved Decision-Making: Agents equipped with relevant context deliver more accurate, nuanced decisions by grounding responses in history, facts, and workflow goals. e.g. An Agent that supports code creation could benefit from the errors and logs but too much of it or poorly curated inputs will likely result in strange results.

• Reduced Hallucination: Poor context leads to hallucination and unreliable behavior. Precise context dramatically improves consistency and trustworthiness. e.g. A patient triage AI Agent might just look at symptom combination of sore-throat and head-ache and speculate meningitis but with the detailed patient specific context it could respond in a far more measured way probably hinting at a viral infection.

• Memory and Continuity: Agents can recall preferences, maintain long-term goals, and evolve intelligently across sessions. e.g. A customer support AI Agent could really shine if it knew all the preferences and past incidents for that user so it doesn’t have to repeat questions and knows what might work best for this user at this moment of time.

• Tool and API Orchestration: Context allows agents to integrate tools, pass outputs forward thereby bridging LLMs with real capabilities. e.g. The output of various tools, external MCP Servers and even other agents could become inputs to the next sub or parent agent when it is carefully infused into the context.

• Adaptability and Personalization: Context engineering enables adaptive behavior, dynamic task management, and deeply personalized interactions. e.g. When a AI Travel Research Agent knows the user’s past itineraries, travel and communication preferences it can deliver valuable results.

• Tool and API Orchestration: Context enables seamless integration of tools and real-world actions bridging LLMs with system capabilities. e.g. When these are carefully embedded into its context an agent can use outputs from MCP servers or other tools as inputs for downstream actions.

• Adaptability and Personalization: Context engineering allows for dynamic behavior and deeply personalized interactions. e.g. A travel planning agent that knows a user’s past itineraries and communication preferences can deliver more relevant suggestions.

The Building Blocks of Context Engineering

To create a truly intelligent agent, context must be engineered across several layers:

• System Prompt: Defines agent role, tone, boundaries.

• User Input: The real-time query or command.

• Short-Term Memory: Recent interactions and decision history.

• Long-Term Memory: Persistent knowledge. E.g: preferences, summaries, past tasks.

• Retrieved Information: External data pulled via RAG, search tools , or external APIs.

• Tool Definitions & Output: Available functions and recent results.

• Structured Output Spec: Response formatting. E.g: JSON

• Workflow State: Awareness of task flow and progress.

All of these form a task-aware context window that lives throughout the life of the agent and evolves as the agent performs its mission through it’s various stages of operations.

Context Engineering Techniques

Modern LLMs have very a large context window. However, just dumping everything you have to the LLM or building articulate prompts won’t suffice as good Agent design requires a strategic context pipeline and following these practices allow agents to be fast, focused, and flexible.

• Context Writing: Constructing the full scene.

• Dynamic Retrieval (RAG): Injecting real-time, relevant facts. e.g. In many situations well organized vector stores of corporate data are the best sources of data.

• Context Compression: Summarizing inputs to fit context window limits. e.g. If you need to share past errors to a coding AI Agent, instead of sharing all error logs just share a meaningful summary for every error in the past.

• Memory Engineering: Retaining and reusing task-critical knowledge. e.g. A virtual Health Coach AI Agent should recall diets, allergies, critical readings, physical activity data and also short term memory from this current session.

• Tool Feedback Loops: Feeding tool output back into the agent’s thinking. e.g. A Monthly Budget Planner Agent might use external to retrieve real-time data and analyze expenses and generate a budget.

• Context Isolation: Avoiding overload by scoping information by task. e.g. In a multi-agent system where each agent handles a different part of legal contract analysis such as compliance, risk assessment etc, every sub agent should be provided only what it needs.

• Temporal Awareness: Tracking what came before and what comes next. e.g. A Product Management AI Agent will need to be aware of what has happened, what is happening now, what is scheduled to happen, what should happen next etc.

When Context Fails, Agents Fail

Majority of agent failures are attributable to context failures. e.g. A customer support agent recommends irrelevant policies because it forgets user history.

• A research agent repeats queries because it lacks task memory.

• With robust context engineering, these same agents could become powerful, reliable collaborators.

A Real-World Example: Executive Job Search Agent

Let’s take a multi-step AI agent built for high-level job seekers (cough cough!!):

• Context Initialization: Loads user resume, preferred roles, job history.

• Retrieval: Pulls live jobs from enterprise career portals.

• Filtering: Applies constraints (remote-only, industry fit).

• Tracking Memory: Logs previously applied roles to avoid duplication.

• Personalization: Tailors cover letters using dynamic resume bullets.

• Tool Usage: Integrates APIs for resume parsing and spreadsheet updates.

With a really good context it could become a valuable autonomous job search agent.

Strategic Benefits for Enterprises

Good context engineering will be the key to success at scale and hence context engineering is fast becoming a C-suite concern, not just a developer practice:

• Productivity: Agents become operationally useful, not just conversational.

• Trust and Governance: Context-aware agents avoid hallucinations and explain decisions.

• Personalization at Scale: Users get responses tailored to role, domain, and history.

• Cost Efficiency: Token budgets are optimized via compression and smart retrieval.

• Multimodal Integration: Unified context across text, voice, tools, and docs.

Context as a Core Discipline

‘Context’ is the control plane and just like DevOps evolved as it’s own discipline within software engineering, ContextOps is likely to do the same and may define AI system maturity.

We should expect to see:

• Context Audits for debugging agent behavior.

• Standard Frameworks for context assembly (LangChain, CrewAI, LlamaIndex).

• Governance Layers for privacy, compliance, and access control.

• Self-reflective Agents that evaluate their own context for quality and gaps.

• Dedicated Context Engineering Roles.

What next?

As we evolve into an agent centric ecosystem, the best agents will be contextually fluent and those organizations that master context engineering will unlock agents that truly understand.