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Do not judge an AI coding agent by a confident sentence saying it ran a command. Judge it by independently visible effects: a file changes, a search returns the right paths, a command produces the expected output and exit status, and a deliberately bad command fails honestly. A reliable check exercises reading, writing, execution, permissions, recovery and persistence in a disposable workspace.
The distinction matters because tool use has several layers. The model may propose a call, while the harness may never send it; the harness may send it, while a sandbox blocks it; or the command may run and fail while the agent reports success. The protocol below lets you separate those cases.
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What does it mean for an AI coding agent to use a tool?
Real tool use is an observable loop, not a promise in the chat. The agent requests an operation, the surrounding harness routes that request under policy, an execution environment performs it, and the result comes back with enough detail for the agent to continue. Your test must therefore inspect the workspace and command results outside the agent’s own narrative.
| Layer | What it does | Typical failure |
|---|---|---|
| Model | Proposes a tool call or an answer. | It emits a plausible command without actually invoking anything. |
| Harness | Supplies tool definitions, applies policy, routes calls, and tracks session state. | A tool appears in instructions but the harness never dispatches the request. |
| Execution environment | Runs a process, edits files, accesses a network, or invokes an MCP server. | The process runs in a different directory, container or remote service than expected. |
| Permissions and sandbox | Decide whether the requested action is allowed and what it can reach. | A command is denied, confined to a workspace, or blocked from the network. |
Microsoft describes the harness as the coordinator of model, tools, permissions and session state. OpenAI’s agent documentation describes sandbox execution, file editing, MCP connections, steering and recovery. Those descriptions establish possible capabilities; they do not prove that a particular session performed an operation.
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How do I test an agent’s real file and command access?
Use a disposable repository and a test identifier that cannot be confused with normal project content. Run every check yourself from a terminal or file manager that the agent cannot rewrite.
1. Record a baseline before asking for changes
Write down the workspace path, repository status, enabled tools, network setting, sandbox mode, approval mode, and model or agent version. Save a clean diff. Note whether the session is local or hosted. This record explains later differences that are caused by configuration rather than by the model.
A useful baseline includes the commit hash, operating system, shell, and the exact directory opened in the agent. If the interface exposes a tool list or policy panel, capture it before the first request.
2. Test reading and writing as separate abilities
Create a disposable file containing a unique marker such as TOOL_PROBE_7842. Ask the agent to locate that marker and replace it with a second unique marker. For example, you can create the fixture outside the agent with:
printf 'TOOL_PROBE_7842n' > tool-probe.txtAfter the agent reports completion, inspect the file and run the version-control diff yourself. A successful read with no filesystem change is not a successful write. A claimed edit without an exact diff is a failed write test, even if the response sounds certain.
3. Test search and directory visibility
Ask for a directory listing and a code search whose answer you already know. Compare the returned paths and matches with an independent shell listing. Include a file outside the obvious source tree if your policy permits it, so you can tell whether the agent sees the whole workspace or only an injected snippet.
Record both false negatives and false positives. A search that misses the known marker shows limited visibility; a result that names paths you did not make may indicate a different working directory, cached context or fabricated output.
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4. Test command execution with a harmless artifact
Request a command that writes a known timestamp or sentinel file. One simple probe is:
printf 'TOOL_RESULT_7842n' > tool-sentinel.txtCheck the file independently, then capture the command’s standard output, standard error and process exit status. The agent’s summary is only one observation; the file and terminal result are the evidence.
5. Request a command that must fail
Ask the agent to read a deliberately missing path, such as cat missing-path-7842. A real tool loop should expose the error, preserve the non-zero status, and adapt or ask what to do next. A red flag is a success narrative with no error output, no exit status, or a fabricated result.
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Do not turn the negative test into a destructive command. A missing file, invalid option in a harmless utility, or a command against a temporary directory is enough to distinguish execution from storytelling.
6. Test network and external tools only in a disposable environment
If web, browser or MCP tools are advertised, request a low-risk public page or a local mock server. Verify the returned content, request log or MCP response independently. GitHub documents browser actions such as navigation, clicking, typing, console inspection and screenshots when browser tools are enabled; the test should confirm which of those actions are enabled in your session rather than assuming the documentation applies unchanged.
Keep credentials, production endpoints and private repositories out of this probe. A network permission can be allow-listed, approval-gated or disabled even when shell execution works.
7. Test interruption, steering and persistence
Stop an in-progress task, send a follow-up instruction, and reopen or resume the session. Check whether the agent retains the actual tool results and workspace state instead of merely repeating its earlier plan. OpenAI documents session state and recovery for its agent harness; your own test should confirm what survives interruption in the product and mode you are using.
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What evidence proves that the tool call really happened?
Use evidence that the agent cannot plausibly manufacture after the fact. The strongest set combines an external artifact with the execution record.
- Filesystem evidence: the expected file exists, contains the expected bytes, and shows the exact diff you requested.
- Search evidence: paths and matches agree with an independent listing or search run outside the agent.
- Process evidence: stdout, stderr and the exit status match the command’s expected behavior.
- Network or MCP evidence: a request log, returned payload, screenshot or local-server log confirms the external operation.
- State evidence: after interruption or resume, the workspace and tool results are still present and correctly described.
- Failure evidence: the deliberately invalid request produces a visible error and causes sensible recovery rather than an invented success.
Keep the observer outside the agent. If the agent is allowed to edit the same log or terminal transcript that you use as proof, the test is not independent.
How do approval prompts and sandboxes affect the result?
Approval and sandboxing are separate controls. An allow-all or autopilot setting can skip prompts while an enabled sandbox still limits terminal filesystem and network access. Conversely, a restrictive approval policy can block a command even when the sandbox would permit it.
VS Code documents separate approval and sandbox controls. GitHub documents approval requirements for potentially destructive Copilot CLI tools. Treat a prompt as a policy event, not as proof that execution occurred: approve a harmless probe, then verify its side effects.
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How should I compare two coding agents fairly?
Run the same probes, in the same type of workspace, with equivalent permissions. Record the dimensions below rather than reducing the comparison to a single claim such as “supports tools.”
| Comparison axis | What to record |
|---|---|
| Tool exposure | Shell, file read/write, search, web or browser, MCP and sub-agent tools. |
| Scope | Workspace-only or arbitrary filesystem; read-only, write, delete and process capabilities. |
| Execution location | Local process, hosted sandbox, container or remote service. |
| Network policy | Disabled, allow-listed, unrestricted or approval-gated. |
| Human control | Per-call approval, rule-based approval, assisted review, allow-all or autopilot. |
| Evidence returned | Exact diffs, stdout and stderr, exit codes, request logs, screenshots and reproducible artifacts. |
| Recovery | Behavior after timeout, denied permission, malformed input or command failure. |
| Persistence | Whether tool results and workspace state survive steering, interruption and resume. |
Hold the prompt, marker names, directory layout and expected answers constant. A model that edits files but cannot report errors is materially different from one that handles both success and failure transparently.
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What capabilities do major coding-agent products document?
Public documentation describes capability sets, not a universal success rate. The following examples show what vendors say their tools can do; your session still needs the probe.
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| Codex CLI | Read, modify and run code on a local machine. | Suggest, Auto Edit and Full Auto approval modes. |
| OpenAI agent harness | Execute code in a sandbox, edit files, connect to MCP servers, steer tasks and preserve session state. | Managed execution and recovery controls are part of the harness. |
| GitHub Copilot CLI | Execute shell commands, read and write files, search code, fetch web content and delegate to sub-agents. | Potentially destructive tools require approval by default. |
| GitHub agent mode | Choose files, make edits, run terminal commands, observe build or test outcomes and iterate. | MCP tools can extend the available operations. |
These are capability examples, not endorsements or performance measurements. Features can vary by edition, account policy, operating system, extension, workspace trust and rollout status, so record the exact version and mode in your baseline.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What failure patterns reveal a fake or blocked tool loop?
The model writes a plausible command but nothing changes
This usually indicates a model-only response, a harness routing failure or a blocked write. Check the tool-call record if available, then inspect the file and diff outside the chat. Do not accept a pasted replacement as evidence of a filesystem edit.
The harness exposes a tool, but the sandbox rejects it
A visible shell or file tool does not imply unrestricted access. Look for permission-denied messages, path restrictions, network errors or approval events. Record the boundary precisely: an agent may have workspace read access while lacking writes, deletes, outbound network or access to a parent directory.
The command runs in the wrong place
Relative paths can make a real execution look like a failure. Compare the agent’s reported working directory with the baseline path, and use an absolute path inside the disposable workspace when policy allows. A sentinel created in another container or checkout is not evidence that the intended repository was changed.
The command fails, but the agent reports success
This is the most serious trust problem. Preserve stderr and the non-zero exit status, then repeat the invalid-command probe. An agent that acknowledges the failure and adjusts is usable under supervision; one that erases or contradicts the error needs a stricter review workflow.
The agent sees an injected snippet rather than the workspace
Give it a marker in a file that was not included in the prompt and ask for a directory-wide search. Compare every returned path with an independent listing. Passing only prompt-visible fixtures does not establish repository access.
Is there a benchmark for successful real-world tool execution?
No comparable, owner-dated, cross-vendor statistic establishes a universal success rate for real-world coding-agent tool execution. Authoritative documentation focuses on capabilities, controls and security rather than one percentage that can be compared across products. Do not manufacture a benchmark from a vendor feature list or from a handful of successful demos.
For a meaningful internal number, define the task set, environment, permission policy, timeout, recovery rules and what counts as success before testing. Report separate rates for reads, writes, commands, failures, network calls and resume behavior; otherwise one aggregate figure hides the exact limitation you need to know.
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Mark an agent as operational only when you can answer every applicable question with an artifact or log:
- Did I record the workspace, version, tools, network, sandbox and approval settings?
- Did the agent find a marker that was not in the prompt?
- Did it change the intended file, with an independently verified diff?
- Did a harmless command create the expected sentinel in the expected directory?
- Did I capture stdout, stderr and the exit status?
- Did a deliberately invalid command fail visibly?
- Did browser or MCP output match an external request or server log?
- Did the task recover correctly after interruption, steering or resume?
- Were credentials and production systems excluded from the experiment?
Any unanswered item is a scoped limitation, not proof that the agent has no tools. Document the boundary so users know which operations still require a human or a different environment.
How should I interpret the final result?
A pass means the requested operation produced the expected external evidence under the recorded policy and the agent handled both success and failure honestly. It does not mean every command, directory, network destination or MCP server is available. A partial pass is often the useful answer: for example, read and search may work in a workspace while writes require approval and outbound network is disabled.
Repeat the probe after changing the model, harness, extension, sandbox, approval rules, operating system or hosting mode. Tool access is a property of that complete configuration, not a permanent property of the agent’s name.
Frequently Asked Questions
How often should I rerun a tool-use probe?
Rerun it after a model, extension, harness, sandbox, approval-policy or hosting change, and after a major version update. Keep the same disposable fixture but generate a new marker so an old artifact cannot masquerade as a fresh result.
What should I preserve for an audit of an agent session?
Keep the configuration snapshot, commit or workspace identifier, prompt and tool-call transcript, file diff, stdout, stderr, exit status and timestamps. Store them outside the agent-controlled workspace so a later edit cannot silently rewrite the evidence.
Why can an agent work locally but fail in a hosted session?
Local and hosted sessions can use different working directories, containers, credentials, network policies and approval rules. Repeat the baseline and sentinel tests in each environment instead of transferring trust from one to the other.
Can a successful edit prove that the agent can run arbitrary commands?
No. A write test establishes only the path and permission used for that operation. Command execution, deletion, network access and access to other directories each need their own scoped probe.
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