Context Engineering: How to Give AI Agents Better Inputs

Start With the Right Questions

Why do AI agents still deliver off-target answers after we polish the prompt? What breaks when a model knows the instructions but not the surrounding situation? Let's treat those questions as our starting checklist, because context engineering begins whenever we doubt whether a prompt alone can carry the task.

The early prompt era looked simple: we typed a well-crafted instruction and expected a complete solution. That approach worked for single-turn chats, but it fell apart once agents had to browse files, call tools, or remember past outcomes. The core insight is that prompts define intent, yet the agent still needs the right supporting facts at the right time.

Our job is to assemble that support system deliberately. We design where the agent should look, what background it must recall, and how often it should refresh its information. Once we treat the interaction like a teaching session instead of a magic trick, the limitations of prompt-only workflows become obvious.

Understand the Limits of Prompt Engineering

Think back to 2022 when ChatGPT conversations were mostly one-off exchanges. A static system prompt was enough because nothing changed between the question and the answer. By 2025, agents operate inside live codebases, monitoring dashboards, and research workflows where inputs keep shifting.

Shopify's CEO Tobi Lütke summed it up by saying he prefers "context engineering" to "prompt engineering." His point is clear: real work depends on the circumstances surrounding each request. If we do not capture that environment, the agent guesses, and guesses often fail.

Notice how the problems feel familiar from human teamwork. A new colleague will struggle unless we help them see past experiments, current constraints, and success criteria. Context engineering simply converts that mentoring mindset into a repeatable, machine-readable practice.

Define the Context Stack

Every dependable agent session carries four layers. We clarify the intent so the model understands the real goal instead of a shallow task description. We write instructions that map the steps and the format we expect.

Then we expose the right tools so the model can pull up-to-date information instead of relying on stale parametric memory. Finally, we supply working memory—summaries of past decisions, errors, and confirmed facts—so the agent does not repeat mistakes.

You can picture this stack as a pilot cockpit. Instruments show current conditions, checklists encode procedures, communication gear reaches outside resources, and flight logs provide history. Leave out any instrument and the pilot must guess; omit any context layer and the agent behaves the same way.

Build Context Dynamically

Static instructions only cover the first turn. Effective agents keep refreshing their context window while the task unfolds. When Cursor rewrites a homepage, it first searches the repository, then narrows the relevant files, then reads key snippets before editing. Each action adds fresh evidence into the working context.

We can script similar behavior with tool calls, retrieval pipelines, or lightweight memories. The goal is to teach the agent to gather and validate evidence before acting. If we see failures, we ask: Did the agent inspect the right resources? Did it capture the observations for future steps?

This dynamic approach mirrors how skilled engineers debug production issues. They log recent incidents, pull telemetry, and cross-check assumptions continuously. Context engineering turns that disciplined loop into an automated pattern the agent can follow.

Keep Humans in the Loop

Even the best models benefit from human oversight because intent drifts and conditions change. We review agent output not to micromanage, but to confirm whether the context is still valid. When something feels off, we feed corrective summaries back into the memory layer.

Use short Socratic prompts with the agent: What assumption did we just violate? Which data point contradicts our plan? These questions reveal missing context faster than broad instructions. They also train the agent to surface uncertainty instead of hallucinating certainty.

Think of yourself as a flight instructor riding along. You let the trainee fly, yet you keep hands near the controls, ready to add guidance or override when the situation shifts. Context engineering thrives on that partnership.

Practice and Keep Improving

Start with small experiments: choose one workflow and document the intent, instructions, tools, and memories explicitly. After each run, ask what information the agent still lacked. Iterate on the context artifacts before expanding to new tasks.

Keep a lightweight log of failures and the missing clues behind them. Over time that log becomes your context playbook, helping agents ramp up on new projects faster than new teammates. Share those playbooks across teams so everyone benefits from the same prepared environment.

Context engineering is not a one-time setup. Just like good onboarding material, it needs updates whenever systems, policies, or goals evolve. The more we refine it, the more our agents behave like reliable collaborators instead of unpredictable magicians.

Support ExplainThis

If you found this content helpful, please consider supporting our work with a one-time donation of whatever amount feels right to you through this Buy Me a Coffee page, or share the article with your friends to help us reach more readers.

Creating in-depth technical content takes significant time. Your support helps us continue producing high-quality educational content accessible to everyone.