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An AI coding agent has genuinely used a tool only when an action produces evidence outside its own reply. A trustworthy check combines a harmless file edit, a read-only search, a command with a known output, and a command that should fail. Verify the file diff, paths, stdout, stderr, exit status, and recovery from an independent terminal or file viewer. A confident sentence such as “I changed the file” proves nothing by itself.
This guide explains what must be tested, how permissions and sandboxes affect results, how to compare agents fairly, and how to spot a plausible-looking but fabricated tool report.
What evidence proves that an AI coding agent really used a tool?
Look for an observable side effect that you can reproduce without trusting the transcript. The strongest evidence is a changed file with the expected diff, a command result that includes independently checked output and an exit code, or a network/MCP request visible in a log. A model can describe a command perfectly without the harness ever dispatching it, so prose is not evidence.
Use independent observation
Keep a terminal, file browser, version-control client, or request log open outside the agent. If the agent is the only place you inspect the result, it can accidentally (or deliberately) report an intended action as a completed one. Independent observation also reveals partial writes, unexpected files, hidden errors, and output that the agent omitted.
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Require a unique, harmless marker
Use a disposable repository and a marker that is unlikely to appear elsewhere, such as TOOL_PROBE_7842. Ask the agent to find that exact text and replace it with a second marker. The combination of a known input, a precise requested change, and an externally visible diff makes the test auditable.
How do the model, harness, environment, and permissions differ?
Tool use is a chain of four layers. A failure at any layer can look like a coding mistake even when the model’s proposed command is sensible.
1. The model proposes an action
The model generates a tool call, command, edit, or answer. It may select the wrong path, use invalid syntax, or claim success before receiving a result. A generated command is an intention, not execution.
2. The harness routes and records the call
The harness supplies tool definitions, applies policy, sends the request to the right service, tracks session state, and returns the result. Microsoft describes this coordination layer as the component that connects the model, tools, permissions, and state. If a tool is shown in the interface but the harness never dispatches it, no side effect occurs.
3. The execution environment performs the work
A local process, hosted sandbox, container, remote worker, or MCP server may execute the request. OpenAI’s Agents API documentation describes sandbox execution, file editing, MCP connections, steering, and recovery as capabilities of a managed harness. The environment determines which filesystem, processes, credentials, and network endpoints are actually reachable.
4. Permissions and sandbox policy allow or block it
Approval settings and sandbox restrictions are separate controls. VS Code documents distinct approval and sandbox choices, while GitHub documents approval requirements for potentially destructive Copilot CLI tools. An action can be approved yet blocked by a sandbox, or run in an allow-all mode while still being confined to a workspace.
How should I establish a safe baseline before testing?
Start by recording the conditions that can change the result. Without a baseline, two agents may appear different simply because one is local and the other is hosted, or because their network and approval policies differ.
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- Use a disposable repository. Copy a small project or create a temporary directory that contains no secrets, production credentials, deployment keys, or irreplaceable files.
- Record the workspace path. Note the absolute path shown by your terminal and the path the agent reports. A relative path can silently point at a different checkout.
- Save a clean diff. Run your version-control status and diff commands before the test. Preserve the output so later changes are attributable to the probe.
- List enabled tools. Record whether shell, file read/write, search, browser, web, MCP, and sub-agent tools are exposed. A missing tool cannot be tested successfully.
- Record policy settings. Write down sandbox mode, network mode, approval mode, model or agent version, and whether the session is local or hosted.
- Keep an observer open. Have a separate terminal or file viewer ready to check every claimed result.
How do I test file reading and writing separately?
Reading and writing are different capabilities. Test them independently so a failed edit does not hide a successful read or vice versa.
Read probe
- Create a disposable text file containing a unique marker such as
TOOL_PROBE_7842in a known directory. - Ask the agent to locate the marker and report the exact file path and line containing it. Do not provide the path in the prompt.
- Search for the marker yourself with an independent shell or file-search tool.
- Compare the agent’s path, line, and surrounding text with your independent result.
A correct answer demonstrates that the agent could see the workspace or an injected representation of it. It does not demonstrate write access.
Write probe
- Ask the agent to replace only the first marker with a second unique marker, for example
TOOL_PROBE_REPLACED_7842. - Inspect the file outside the chat and run a version-control diff.
- Confirm that the old marker disappeared, the new marker appears exactly once, and unrelated lines are unchanged.
- Ask the agent to state which file it changed, then compare that statement with the diff rather than accepting it as proof.
If the transcript says the edit succeeded but no external change exists, the write test failed. If the file changed but the agent reports the wrong path or content, the harness may have applied an action while the model misread the result.
How can a directory listing and code search reveal limited visibility?
Agents sometimes receive a pasted snippet or a narrow virtual workspace instead of the complete directory. A known-answer search and an independent listing expose that limitation.
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- Place files with distinctive names in two directories inside the disposable workspace, including one nested several levels deep.
- Ask the agent for a directory listing from the workspace root without naming those files.
- Run an independent recursive listing and compare paths, hidden files, and nesting.
Missing paths may indicate an intentionally restricted workspace, an ignored directory, a path mismatch, or a failed listing command. Ask the agent to distinguish those possibilities rather than filling gaps with guesses.
Search test
- Put a known phrase in one source file and a similar phrase in another file.
- Ask for a code search for the exact phrase and request every matching path and line.
- Repeat the search independently and compare case sensitivity, ignored files, binary-file handling, and generated directories.
A result that names only a snippet already shown in the conversation is weaker evidence than a result that discovers an unseen file and reports its exact line.
What command-execution test distinguishes real output from a fabricated answer?
Use one command that should succeed and another that should fail predictably. Check the filesystem, stdout, stderr, and exit status yourself.
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Successful command
- Ask the agent to run a harmless command that writes a known sentinel or timestamp to a disposable file. The command should not install software, alter configuration, or access secrets.
- Request the exact command, working directory, captured stdout, captured stderr, and exit code.
- Check the sentinel file and its contents from an independent terminal.
- Compare the independently observed timestamp or text with the agent’s report.
A missing file, absent output, or unreported error means the test did not establish successful execution. A command may have run in a different working directory, so verify the path as well as the content.
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Deliberately failing command
- Ask the agent to reference a path that definitely does not exist in the disposable workspace.
- Verify that the process returns a nonzero exit status and that the error appears on stderr or in the tool result.
- Observe whether the agent explains the failure, corrects the path only when instructed, and avoids claiming success.
Real tool loops surface failures and adapt. Fabricated success, a zero exit code for an impossible path, or an error that appears only after you challenge the agent is a red flag.
How should I test web, browser, or MCP tools?
Only test external access in a disposable environment. Network capability can expose credentials, upload source code, or trigger an external action, so a public, low-risk page or a local mock server is safer than a production endpoint.
- Confirm the advertised browser, web, or MCP tool and its network policy.
- Ask the agent to retrieve a small, public page or query a local mock server with a unique response string.
- Check the returned content against the page, server log, request method, path, and timestamp.
- For browser tools, verify navigation, clicking, typing, console inspection, and screenshots separately; GitHub documents these actions when browser tools are enabled.
- For MCP, compare the tool name, arguments, response, and server log. A pasted answer is not proof that the MCP server was contacted.
Stop if the tool requests credentials, uploads repository contents, follows an unexpected redirect, or attempts a state-changing operation. A network-disabled result is not a defect if the recorded policy intentionally blocks external access.
How do interruption, steering, and resume tests expose fake persistence?
A capable agent should preserve actual tool results and workspace state, not merely repeat its earlier plan after interruption.
- Start a task that performs a harmless, observable action in the disposable repository.
- Interrupt it before completion and inspect whether any partial file or process remains.
- Send a follow-up that asks what actually happened, including the last tool result and exit status.
- Reopen or resume the session and ask the agent to continue from the current workspace state.
- Compare its account with the independent diff, process list, and tool logs.
OpenAI’s Agents API materials describe session state and recovery, but persistence is a property of the specific harness and configuration. A resumed conversation that remembers a plan while losing the real filesystem state has conversational memory, not reliable execution persistence.
How should I compare two coding agents fairly?
Run the same probes, in the same disposable repository, under documented policies. Record capability and evidence rather than relying on a single success anecdote.
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| Comparison axis | What to record | Evidence to retain |
|---|---|---|
| Tool exposure | Shell, file read/write, search, web or browser, MCP, and sub-agents | Tool list and the exact probe used |
| Scope | Workspace-only or broader filesystem; read-only, write, and delete rights | Observed paths and diffs |
| Execution location | Local process, hosted sandbox, container, or remote service | Working directory, environment details, and logs |
| Network | Disabled, allow-listed, unrestricted, or approval-gated | Request log or a documented block |
| Human control | Per-call approval, rule-based approval, assisted review, allow-all, or autopilot | Prompt history and policy setting |
| Evidence quality | Whether results include exact diffs, stdout, stderr, exit codes, screenshots, and reproducible artifacts | Saved outputs checked outside the agent |
| Recovery | Behavior after timeout, denied permission, malformed input, or command failure | Failure transcript plus resulting state |
| Persistence | Whether tool results and workspace state survive steering, interruption, and resume | Before-and-after diffs and session records |
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What do approval prompts and sandboxes actually tell me?
Approval shows that a policy gate was reached; it does not prove that the command executed. Sandboxing determines what the process can access, independently of whether a person approved the request.
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Approval is a human-control mechanism
VS Code warns that agent actions can use the user’s permissions to create, modify, or delete files, run shell scripts, install software, access credentials, change infrastructure, push code, call APIs, and trigger deployments or financial transactions. Approval prompts reduce accidental actions, but an approved request can still fail in the environment.
Sandboxing is an execution boundary
An allow-all or autopilot setting may skip prompts while an enabled sandbox still limits terminal filesystem and network access. Conversely, a permissive sandbox with mandatory approval can block execution until a person confirms it. Record both settings for every test.
Use least privilege
- Run probes with disposable files and credentials that have no production value.
- Prefer workspace-only access and a restricted network policy.
- Do not grant broad delete, deployment, payment, or credential permissions merely to make a demo succeed.
- Keep a human-controlled terminal available to stop processes and revert changes.
What capabilities do current official examples document?
Official documentation describes what several popular harnesses are designed to do; it does not guarantee success in every installation, plan, operating system, or policy configuration.
| System or mode | Documented capabilities | What your probe must still verify |
|---|---|---|
| Codex CLI | OpenAI says it can read, modify, and run code on a local machine, with Suggest, Auto Edit, and Full Auto approval modes. | Whether the installed CLI has the expected mode, workspace path, permissions, and process access. |
| OpenAI Agents API harness | Provides managed sandbox code execution, file editing, MCP connections, steering, and session recovery. | Which tools are enabled for your agent, how state is persisted, and what the sandbox or network policy blocks. |
| GitHub Copilot CLI | GitHub says it can execute shell commands, read and write files, search code, fetch web content, and delegate to sub-agents; potentially destructive tools require approval by default. | Whether approval is enabled, which commands are classified as destructive, and whether results appear in the local workspace. |
| GitHub agent mode | Chooses files, makes edits, runs terminal commands, observes build or test outcomes, and iterates; MCP tools can extend it. | Whether the selected files, test commands, and MCP servers are the ones you expect in your environment. |
These descriptions establish intended capability, not a cross-vendor success rate. The only dependable conclusion for your setup comes from the same external probes applied under the same conditions.
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Authoritative capability and security descriptions do not provide a comparable, owner-dated statistic for successful real-world tool execution across vendors. Success depends on the model, harness, repository, operating system, sandbox, network, approval policy, and task difficulty. A percentage drawn from one benchmark or vendor’s own environment would not answer whether your agent can modify your files safely.
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Report the evidence you can reproduce: exact diffs, command outputs, exit codes, request logs, screenshots, and recovery behavior. Do not convert a few successful demonstrations into a universal reliability claim.
How do I troubleshoot a claimed action that left no evidence?
Work from the outside in, checking each layer instead of repeating the same prompt.
- Confirm the path. Compare the agent’s working directory with the absolute path in your independent terminal. A second checkout is a common explanation.
- Check the initial state. Verify that the marker existed before the read or edit request and that your observer is viewing the same branch and container.
- Inspect tool dispatch. Look for a real tool-call record, not only natural-language narration. If no call was dispatched, the failure is at the model or harness layer.
- Read stderr and the exit code. A command may have run and failed. Missing stderr in the agent’s summary does not make it successful.
- Check policy blocks. Review approval prompts, sandbox denials, read-only mounts, ignored paths, and network restrictions.
- Look for delayed or partial effects. A process may still be running, or a write may have stopped halfway. Inspect files and processes before retrying.
- Retry with a smaller probe. Use one file, one marker, and one command. Avoid changing several variables at once.
If the agent repeatedly reports success without a matching artifact, treat tool use as unverified and do not grant it broader permissions.
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Save this checklist with each agent evaluation:
- Workspace path, repository revision, and clean starting diff
- Model or agent version and session type (local or hosted)
- Enabled tools and their documented scope
- Sandbox, network, and approval settings
- Unique read marker and the agent’s discovered path
- Before-and-after file contents and version-control diff
- Successful command, working directory, stdout, stderr, and exit code
- Predictably failing command and the agent’s recovery behavior
- Independent directory listing and search comparison
- Browser, web, or MCP request evidence, if those tools are enabled
- Interruption, steering, and resume results
- Any denied permission, timeout, malformed input, or partial side effect
Keeping these artifacts turns “the agent said it worked” into a reproducible capability record that another person can audit.
Frequently Asked Questions
Can an AI coding agent edit files without showing a visible tool call?
It can operate through a hidden or summarized harness, but you should still be able to verify the resulting filesystem diff or another external artifact. If no independent side effect or execution record exists, treat the edit as unproven regardless of how confidently it is described.
What should I do if a sandbox blocks a harmless command?
Record the denial, the sandbox rule, and the command’s intended scope first. Then move the probe to an allowed disposable directory or adjust only the minimum policy needed; do not disable all isolation merely to obtain a successful demo.
How can I tell whether an agent is seeing my whole repository or only pasted context?
Ask it to discover an unusual marker in a file you did not mention and to list a nested directory that was not included in the conversation. Compare both answers with an independent recursive listing and search; failure to find unseen files indicates limited visibility or a path mismatch.
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Is a successful build or test enough to prove tool use?
No. A build result can come from cached output, a different checkout, or a command run by another service. Tie the result to a fresh command log, verified working directory, exit code, and an artifact such as a changed file or newly generated report.
How often should I repeat these probes after updating an agent?
Repeat the full baseline after changes to the model, harness, sandbox, operating system, network policy, or approval mode. For routine sessions, a small read/write marker and a known failing command provide a quick regression check before granting access to valuable repositories.
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