As artificial intelligence matures, so does the art—and necessity—of context engineering. In her video “Context Engineering Clearly Explained,” Tina Huang offers both a definition and a practical guide to this emerging discipline, showing why it’s considered the natural successor to prompt engineering and central to building effective AI agents today.
What Is Context Engineering?
Context engineering is the practice of designing and assembling dynamic input systems that deliver the right information, in the right format, at the right time to a large language model (LLM). In essence, while prompt engineering crafts clever questions or cues for LLMs, context engineering concerns itself with orchestrating the entire context window—the full set of data, instructions, and structures that guide an AI agent to accomplish its task.
Context engineering shines when building robust AI-powered applications (think multi-functional AI agents that must act reliably without human-in-the-loop prompting).
Prompt engineering remains vital for conversational interactions (think chatbots).
Context engineering shines when building robust AI-powered applications (think multi-functional AI agents that must act reliably without human-in-the-loop prompting).
Why Did Context Engineering Emerge?
As AI applications shifted from simple chatbots to complex, multi-step agents (e.g., customer service bots, coding assistants, or research synthesizers), the limits of prompt engineering became apparent. These applications need to:
- Handle diverse user queries.
- Access tools and databases.
- Remember prior interactions or facts.
- Act autonomously, often in unpredictable situations.
Hence, rather than iteratively “prompting” an AI, creators began designing intricate input structures resembling configuration files, codes, or playbooks. This new discipline is context engineering—a kind of instruction manual that connects tools, memory, safety mechanisms, and orchestrations within an AI agent.
The Six Building Blocks of AI Agents
Tina Huang likens AI agent architecture to building a burger: various ingredients are essential. The six core components are:
| Component | Description |
|---|---|
| Model | The underlying AI/LLM (e.g., GPT-4, Claude, Gemini, open-source) |
| Tools | Plugins or APIs for external actions (e.g., calendar, search) |
| Knowledge & Memory | Systems to store/retrieve information or context over time |
| Audio & Speech | Capabilities to process/produce speech and audio |
| Guardrails | Rules and filters for safety and compliance |
| Orchestration | Systems to deploy, monitor, and refine agent behavior |
A sophisticated agent may have all six, but what truly matters is defining how these parts fit together—that’s where context engineering comes in.
The Role of the Context Engineer
Think of the context engineer as the author of the agent’s instruction manual—detailing how to use each tool, when to invoke memory, how to escalate conversations, and how to respond to edge cases. This “manual” is encapsulated as a structured system prompt that can include:
- Roles and tasks: What the agent is, and what it’s supposed to do.
- Step-by-step logic: Explicit instructions for gathering, filtering, and outputting information.
- Formats: Prescribed JSON structures, markup tags, or API call protocols.
- Constraints and reminders: What to include/exclude, how to prioritize, what tools to use.
Practical Example: Building an AI Research Assistant
- Role: Summarize AI trends
- Steps:
- Extract diverse sub-tasks from the main query;
- Prioritize by engagement and source authority;
- Output a JSON array with required metadata;
- Adhere to strict output format and time constraints.
- Input & Output: Heavily structured using XML tags and JSON—allowing deterministic, composable, and extensible agent behavior.
Techniques and Resources for Advanced Context Engineering
For those designing multi-agent systems, key principles include:
- Sharing context between agents: Ensuring continuity and collaboration.
- Explicitly handling decision points: Building logic for branching and fallback behavior.
Common context engineering strategies:
- Writing context: Agents record task-relevant info for future use.
- Selecting context: Pulling in external resources on-demand.
- Compressing context: Techniques to fit more info within the LLM’s limits.
- Isolating context: Partitioning context for modular, parallel activities.
Recommended resources include in-depth blog posts by Cognition and Langchain, which cover both architecture theory and case studies.
Why Does Context Engineering Matter?
As AI becomes integral to real-world, production-grade software, context engineering is no longer optional. It’s vital for:
- Reliability and reproducibility (agents do what you intend, every time).
- Safety and compliance (via guardrails and structured logic).
- User satisfaction (agents handle nuanced queries and complex tasks, not just canned responses).
- Scalability (from one-off chatbots to fleets of autonomous agents).
In summary:
Context engineering represents a major step forward in building AI agents that are robust, reliable, and autonomous. By thoughtfully structuring inputs and orchestrating all the agent’s capabilities, context engineering unlocks the full potential of large language models in real-world applications.