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Build an AI code-generation tool as an application around a model, not as a single prompt. Your application should collect a narrowly defined task, assemble repository context, call the model, dispatch typed tools, optionally run code in an isolated workspace, return a reviewable diff, and record the outcome. Start with one bounded workflow—such as explaining a file or proposing a function—before adding autonomous multi-file editing.
The architecture you actually need
A useful coding product has six cooperating layers:
- Task interface: accepts the request, repository or project selection, constraints, and acceptance criteria.
- Context assembler: retrieves relevant files, symbols, dependency information, and project instructions instead of placing an entire repository in one prompt.
- Model runtime: generates an answer or chooses the next tool call.
- Tool gateway: exposes narrow, typed operations such as search, file reading, patch creation, test execution, and diff retrieval.
- Workspace: an isolated environment for tasks that must edit files or run commands.
- Review surface: shows the proposed diff, test results, logs, and errors so a developer can approve, amend, or reject the work.
Keep authorization, side effects, and state in your application. The model should request an operation; your server decides whether that operation is allowed and executes it.
1. Define a first task and acceptance criteria
Choose one capability with a measurable result. Good first releases include “explain this file,” “generate a function in this module,” or “fix this failing test.” A repository change should state:
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- the files or directories the agent may inspect;
- whether it may edit files and run commands;
- the expected behavior and non-goals;
- the exact checks that determine success; and
- what a reviewer will receive, such as a patch and test output.
Well-scoped tasks make failures diagnosable. An open-ended request to “improve the codebase” is difficult to authorize, evaluate, or undo.
2. Choose how the model loop is orchestrated
| Approach | Best fit | Trade-offs |
|---|---|---|
| Direct model API with your own loop | Short tasks and products needing precise control over state, tools, retries, and UI | You implement turn management, function dispatch, limits, persistence, and error handling |
| Agent SDK with a managed runtime | Multi-step work needing managed turns, function tools, guardrails, handoffs, sessions, or tracing | Less loop code, but you still choose permissions, tools, context policy, and review rules |
These choices can coexist. You might use a managed agent runtime for interactive repair while retaining an application-owned service for indexing, authorization, and final merge decisions. Define a maximum number of turns, tool calls, wall-clock time, and output tokens so a stalled task cannot run indefinitely.
A minimal application-owned loop
The following Python skeleton shows the control flow. Supply a model adapter for your chosen API; the surrounding policy and tool execution remain yours.
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from dataclasses import dataclass, field
from typing import Any, Callable
@dataclass
class State:
messages: list[dict[str, Any]] = field(default_factory=list)
calls: int = 0
class CodingTool:
def __init__(self, name: str, schema: dict, handler: Callable[[dict], dict]):
self.name, self.schema, self.handler = name, schema, handler
class CodeAgent:
def __init__(self, model, tools: dict[str, CodingTool], max_turns: int = 12):
self.model, self.tools, self.max_turns = model, tools, max_turns
def run(self, task: str, context: str) -> State:
state = State(messages=[{
'role': 'system',
'content': 'You propose reviewable changes. Use tools only within policy.'
}, {
'role': 'user',
'content': f'Task:n{task}nnRepository context:n{context}'
}])
for _ in range(self.max_turns):
reply = self.model.complete(state.messages, [t.schema for t in self.tools.values()])
state.messages.append(reply)
if reply.get('type') == 'final':
return state
if reply.get('type') != 'tool_call':
raise RuntimeError('Unexpected model response')
tool = self.tools.get(reply['name'])
if tool is None:
raise PermissionError('Tool is not enabled')
state.calls += 1
result = tool.handler(reply['arguments'])
state.messages.append({
'role': 'tool', 'name': tool.name, 'content': result
})
raise TimeoutError('Turn limit reached; return the current trace for review')
In production, validate arguments against the schema, enforce path and command policies, redact secrets from logs, and persist the trace after every turn so a disconnected client can reconnect.
3. Assemble repository-aware context
Code depends on project structure, symbols, imports, build commands, generated files, and local conventions. Context retrieval should be targeted:
- Identify the task’s likely files from the request, symbol names, stack traces, and repository search.
- Read relevant files and nearby tests, configuration, and documentation.
- Include dependency relationships and the commands used to validate the change.
- Trim unrelated material and preserve file paths and line ranges so citations in the review are meaningful.
- Refresh context after an edit or failed test; stale snippets cause the model to reason about code that no longer exists.
A snippet-only explainer may not need a shell or editable workspace. A bug fixer generally does. Repository-level benchmark research uses contextual dependencies and a separate sandbox for each task; its reported average of about 3.1 dependencies applies only to samples in the CODEAGENTBENCH dataset, not to arbitrary repositories.
Useful first-party tools
- search_repository: accepts a query, path scope, and result limit.
- read_file: accepts a normalized path and line range.
- propose_patch: returns a patch for review rather than silently writing files.
- run_checks: accepts an allow-listed command and timeout.
- get_diff: returns changed files, hunks, and status.
Use typed inputs and outputs. Never let the model construct an unrestricted shell command or arbitrary filesystem path.
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4. Decide whether to provide compute
| Execution model | Use it when | Responsibility |
|---|---|---|
| No compute environment | The product only explains code or returns snippets | Lower risk and simpler operations, but no runtime feedback |
| Hosted sandbox | The task needs files, package installation, builds, or tests | The service manages much of provisioning and isolation; verify its limits and persistence behavior |
| Self-hosted sandbox | You need private networking, custom software, or infrastructure control | You own provisioning, reconnect, shutdown, patch persistence, and isolation |
Give each task a fresh workspace where practical. Capture the base revision, applied patch, command output, exit codes, and final diff. Do not merge generated changes automatically merely because a command returned zero.
5. Treat execution as a security boundary
OpenAI’s sandbox security documentation states: “Agent-generated code can access the files, credentials, and network available to its environment.” Design for that outcome.
- Run workloads in isolated compute and separate environments that must not share data.
- Mount only the repository and temporary directories the task needs.
- Allow outbound traffic only to approved endpoints; deny cloud metadata services and unrelated internal networks.
- Keep application keys outside the agent environment. For third-party APIs, use a trusted proxy or application-side function handler rather than placing a long-lived secret in an environment the generated code can read.
- Run as a non-root user, apply CPU, memory, disk, process, and wall-clock limits, and destroy the workspace after retention requirements are met.
- Classify logs and redact source, credentials, and personal data before sending traces to an observability system.
Make every permission visible in the review UI: readable paths, writable paths, network policy, and commands that actually ran.
6. Build review into the product
Return a structured result rather than a chat paragraph:
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- files changed and a unified diff;
- tests, lint, and build commands with exit status and output;
- tool errors, skipped checks, and remaining uncertainty; and
- an explicit approve, request-changes, or discard action.
GitHub’s Copilot Agents responsible-use guidance says to carefully review and test generated code. Require that review even when automated checks pass: tests can miss security flaws, incorrect assumptions, and untested behavior.
7. Evaluate the whole workflow
Create a representative task set covering the features you intend to ship: explanations, new functions, bug fixes, and multi-file changes if those are in scope. Run repeated trials because model output varies. Track:
- Task resolution: whether the acceptance criteria were met.
- Token efficiency: useful work relative to context and output consumed.
- Latency: time to first response and time to a reviewable result.
- Tool reliability: malformed calls, retries, timeouts, and handler failures.
- Runtime checks: tests, lint, type checks, and build success appropriate to the project.
Inspect diffs and logs, not just final prose. There is no topic-wide performance number that predicts how a newly built tool will perform; model, repository, prompt, permissions, and checks all change the result.
8. Make the service observable and recoverable
Stream progress to the client or expose lifecycle webhooks for queued, running, waiting-for-review, succeeded, failed, and canceled states. Persist an idempotency key for each task so a retry does not apply a patch twice. A function-tool handler must always return a result or a bounded error; a missing response can leave an agent waiting forever.
Record task identifiers, model configuration, context selection, tool calls, durations, exit codes, and outcome labels. Keep source and secrets out of general logs, and provide a way to export a complete trace for an authorized reviewer.
9. Control latency and operating cost
- Start with a small context window and fetch more files only when evidence requires it.
- Cache stable repository metadata, but invalidate it after writes and branch changes.
- Use bounded retries for transient model, package, and network failures; do not retry deterministic validation errors.
- Cancel queued work when the user abandons it and enforce per-task budgets.
- Measure tokens, tool calls, sandbox time, and storage per successful task before choosing defaults.
- Re-evaluate those measurements whenever you change model, SDK, retrieval policy, or runtime image.
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Troubleshooting common failures
The model edits the wrong file
Cause: context retrieval returned ambiguous or stale matches. Fix: include canonical paths, symbol definitions, line ranges, and a fresh search after each write. Require the tool to return the path it changed and reject paths outside the task scope.
The agent loops on the same tool call
Cause: the handler returns an unhelpful error or the model cannot observe state changes. Fix: return structured error codes and relevant output, detect repeated arguments, cap turns, and expose current diff and workspace status.
Tests pass but the change is unsafe
Cause: tests do not cover security, authorization, malformed input, or production configuration. Fix: add security-focused checks, inspect the diff manually, and test with representative hostile inputs before approval.
Commands hang or consume the host
Cause: missing time, process, memory, or network limits. Fix: execute through a supervisor, apply hard quotas, terminate the process tree on timeout, and destroy the workspace.
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The user loses a completed task after disconnecting
Cause: state lived only in the request process. Fix: persist task state and artifacts after every model turn and provide reconnectable status or webhook delivery.
Screenshot capture is cluttered
Cause: consent UI, newsletter overlays, or chat widgets loaded before capture. Fix: let ScreenshotNeo handle those elements, or configure its individual cleanup steps and waits; inspect the X-Page-Verdict and X-Billed headers when diagnosing a response.
Best Value
FAQ
Should the first version be an autonomous coding agent?
No. A bounded generator with explicit context, a patch, and human approval gives you safer feedback about retrieval, tool design, and evaluation before autonomy expands the failure surface.
Do I need a sandbox for every code-generation feature?
No. Explanations and snippet generation can run without compute. Editing, dependency installation, builds, and tests require an isolated workspace with appropriate limits.
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Save the task specification, selected context, model responses, validated tool arguments, tool outputs, workspace revision, command results, timings, and final review decision, while redacting secrets and sensitive source.
Can automated tests approve a generated patch?
They can provide evidence, not complete approval. A developer should inspect the diff, test behavior and security, and decide whether to merge it.
Bottom line
The reliable pattern is a narrow task contract, targeted repository context, typed tools, isolated execution where needed, explicit security boundaries, repeatable evaluation, and a human review gate. Build that loop first; add autonomy only when your measurements show it is safe and useful.
Frequently Asked Questions
Should the first version be an autonomous coding agent?
No. Start with a bounded generator that returns a reviewable patch and evidence.
Do I need a sandbox for every code-generation feature?
No. Use one when the task edits files, installs dependencies, or runs commands.
What should I save for debugging?
Persist task context, model and tool traces, workspace revision, command results, timings, and review decisions with secrets redacted.
Can automated tests approve a generated patch?
Tests are evidence; a developer still needs to inspect and approve the change.
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