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To get better results from a coding agent, shape more than its opening prompt: give it clear project guidance, useful tools, access to relevant code when it needs it, a way to retain decisions across long tasks, and feedback from tests and review. The wording of a request matters, but the information and capabilities available to the agent throughout the work matter too.
What context engineering adds to prompt engineering
Prompt engineering is the work of writing and organizing instructions for a model. Context engineering is broader: it involves curating and maintaining the information available during inference, including instructions, tools, external data, and conversation history. Anthropic describes it as “the set of strategies for curating and maintaining the optimal set of tokens” in its September 29, 2025 article on context engineering for AI agents.
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For a coding agent that takes actions over several turns, context is not just a static bundle supplied at the start. Tool calls return new information, files change, and the next useful instruction or piece of evidence may depend on what has happened. Context selection therefore needs to continue during execution.
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“Context engineering beats prompt engineering” is best understood as a useful framing for agentic work, not a universally standardized taxonomy or a claim that prompts are unimportant. Clear instructions remain necessary; they are one part of the broader information and tool environment.
Should you give a coding agent the whole codebase?
Usually, do not load every file by default. A large amount of text does not guarantee that the agent will focus on the files that matter. A more practical approach is to provide stable, high-signal project guidance up front, then let the agent find task-specific files through search, file-reading, or other retrieval tools.
This hybrid approach trades some upfront context for exploration. It can preserve room for relevant details, but it depends on tools and search habits that keep the agent from wandering. For a small task with a known file, providing that file directly may be simpler. For a cross-cutting change, the agent may need to inspect multiple components and their tests before making a plan.
Build a practical context workflow
1. Write project guidance that answers the agent’s real questions
Give the agent the goal, constraints, expected deliverable, and conventions it needs to work safely. Organize longer guidance into named sections so relevant rules are easy to locate. Include important requirements such as supported versions, design boundaries, commands for checks, and files or interfaces that must not change when they apply to the task.
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Start with a sufficient baseline rather than trying to anticipate every possible mistake. When the agent repeatedly misses a convention or produces a particular failure, add the missing instruction or a canonical example. Minimal guidance is useful only if it still contains the information needed to do the work.
2. Make the tools understandable and useful
Tools define what the agent can inspect and change. A tool should have a clear purpose, comprehensible parameter names, predictable output, and useful error messages. Overlapping tools with unclear differences make it harder for an agent to choose the right action.
Test the agent’s actual tool use, not just whether a tool exists. If it repeatedly supplies the wrong arguments, misses information in a result, or cannot recover from an error, improve the interface or its description. In its account of building a SWE-bench agent, Anthropic wrote, “we actually spent more time optimizing our tools than the overall prompt.” That is a vendor’s description of its own engineering work, not a controlled comparison proving tools always matter more than prompts.
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3. Retrieve code when it becomes relevant
Give the agent stable instructions and a route to discover task-specific details. It can begin with the stated goal, inspect the project structure, search for relevant symbols or behavior, and read the implementation and tests that bear on the change. This just-in-time approach can work better than filling the initial context with files that may not matter.
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Use retrieval deliberately: ask the agent to identify relevant files and explain why they matter before a broad edit, and make sure it can follow references from a symbol to its callers or tests. If the task is tightly scoped and the relevant files are known, supplying them directly may avoid unnecessary searching.
4. Preserve state on work that spans many turns
Long tasks benefit from a compact progress note or task list that records decisions, unresolved questions, completed checks, and the next step. A useful note preserves what the next session needs to continue, rather than repeating a transcript of every tool call.
Summarizing or compacting context can remove redundant results, but aggressive compression can erase a detail that becomes important later. Anthropic describes specialized subagents returning condensed findings of around 1,000–2,000 tokens in one architecture. Treat that as an illustrative practice, not a universal target for summaries. A subagent is most useful when it can investigate a focused question independently and return findings worth the coordination overhead.
5. Close the loop with tests and review
Let the agent observe the results of its changes. Run relevant tests, inspect failures, and let the agent use that evidence to correct its work. A test result can provide concrete feedback about behavior; it cannot establish that every broader product, security, or compatibility requirement has been met.
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Best Value
Choose a runtime by control, state, and execution
Runtime labels alone do not tell you how a coding agent will behave. OpenAI’s documentation distinguishes a managed Agents API runtime, an Agents SDK for application-controlled agent loops, and the Responses API for direct model integration. The practical differences are who operates the loop, how state is handled, where tools run, and how approvals and feedback are managed.
| Setup | Who controls the loop | State and execution considerations |
|---|---|---|
| Managed Agents API runtime | The service manages the agent runtime. | Check the documented state-handling and execution model for the specific product and configuration; these details can change. |
| Agents SDK | Your application controls the agent loop. | Your application has more responsibility for orchestration and state. Decide where code and tools execute and how approvals are enforced. |
| Responses API | Your application integrates directly with the model and manages the surrounding workflow. | Plan how to retain conversation state, run tools, and provide results back to the model. |
This is a decision framework, not a claim that one runtime is best. Compare current vendor documentation before implementation, since interfaces and availability can change. OpenAI’s Agents documentation describes its product-specific options.
Add integrations deliberately and protect credentials
Function calling, MCP, Skills, shell access, file search, and tool search are different ways to provide actions or information; they are not interchangeable labels for the same capability. Choose an integration based on what the agent needs to do, where the relevant data lives, and what permissions are appropriate.
For MCP connections, establish whether the service or the agent environment connects to the server, and verify network reachability, configuration, credentials, and allowed tools. Keep secrets out of reusable agent definitions and logs. OpenAI’s tools guide and remote MCP guide document product-specific details; confirm current interfaces and availability before relying on them.
Quick Recap
A quick diagnosis when an agent’s output is poor
- It misunderstands the task: clarify the goal, constraints, and expected result in project guidance or the task request.
- It edits the wrong area: improve repository discovery and retrieval, or provide the known relevant files for a narrowly scoped task.
- It uses tools incorrectly: make tool purpose, parameters, outputs, and error handling clearer, then observe whether the agent can use them reliably.
- It forgets earlier decisions: maintain a concise progress note with decisions, unresolved issues, and next steps.
- It produces plausible but incorrect code: provide test feedback, inspect the diff, and have a person review requirements that tests do not cover.
- It searches without making progress: narrow the investigation question, set a useful next action, or supply more specific file or symbol references.
What to remember
- Prompt wording is one part of the agent’s context, not the whole operating environment.
- Relevant information is more useful than indiscriminately loading more files.
- Tools, retrieval, and persistent task state shape what an agent can do across turns.
- Tests provide feedback, while human review remains necessary for requirements that automated checks do not settle.
- Choose a runtime and integrations by control, state, execution, and permissions—not by label alone.
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