Agent Harness Fault Injection

Agent Harness Fault Injection

Overview

Use a deterministic, non-production fault schedule to test whether an agent workflow preserves state, budgets, safety boundaries, and evidence when a dependency fails. The output is a small fault matrix, an event timeline, and a verdict that distinguishes recovered, contained, unrecoverable, and inconclusive runs.

When to Use This Skill

• Use when a multi-step agent, state machine, loop, or multi-agent workflow has a new recovery path.
• Use when sandbox execution, an MCP/tool call, a worker, a checkpoint store, or memory can time out or disappear.
• Use before claiming retry, resume, deadline, isolation, or partial-failure behavior is production-ready.
• Use when a regression needs reproducible failure evidence instead of a random chaos run.

Do not use this skill against a production target, real user data, live credentials, or an unbounded external service. Convert those cases to a local simulator or an authorized staging harness first.

Safety and Boundary Preconditions

1. Freeze the workflow revision, model/prompt configuration, tool schemas, seed, input fixture, timeout, retry budget, deadline, and expected terminal states.
2. Run in a disposable sandbox with synthetic inputs and stubbed tools. Keep network disabled unless the test explicitly needs a local test server.
3. Make every injected failure an in-memory or fixture-controlled event. Never delete real data, revoke real credentials, kill an unrelated process, or mutate a live service to create a failure.
4. Record the test scope and a run identifier before starting. A missing scope, fixture, or recovery contract makes the verdict inconclusive.

Recovery Contract

Write the invariant before injecting a fault. A useful contract names the state that must survive and the side effects that must not repeat:

After recovery, resume from the latest durable checkpoint, preserve the task
identity and safety policy, spend no more than the remaining retry/deadline
budget, and commit each externally visible effect at most once.

Model the workflow with explicit states. For example:

created -> running -> checkpointed -> waiting_for_tool
                       |                |
                       v                v
                    failed <--------- recovering -> resumed -> completed

For each transition, define the owner, durable fields, allowed retry count, and terminal behavior. In-memory values are not checkpoints unless the harness proves they survive the simulated restart.

Fault Matrix

Select the smallest set of faults that covers the new recovery logic. Do not randomize the schedule until a deterministic schedule has passed.

Deterministic Injection Schedule

Use event numbers rather than wall-clock randomness. A schedule should be portable across harnesses:

{
  "seed": "harness-fixture-07",
  "faults": [
    {"event": "tool.call", "ordinal": 2, "kind": "timeout", "tool": "search"},
    {"event": "worker.start", "ordinal": 2, "kind": "restart"},
    {"event": "branch.join", "ordinal": 1, "kind": "partial_failure", "branch": "summarize"}
  ]
}

The harness should emit the schedule, not merely the seed. Keep fault identity separate from the observed error so a wrapper cannot accidentally turn a timeout into a generic failure. Run the same schedule twice and compare the normalized timeline before trying a different schedule.

Recovery Rules by Boundary

Sandbox and MCP/tool failures

• Assign a request id and idempotency key before the call.
• Distinguish timeout, explicit tool error, invalid output, and policy denial.
• Retry only the declared retryable classes; preserve the original error and attempt count in the evidence.
• Do not retry a side effect unless the tool contract says the key is safe to replay. A read timeout is not proof that a write did not happen.
• When the deadline or retry budget is exhausted, emit one terminal event and stop scheduling work.

Worker restart and checkpoints

• Persist task id, workflow version, state name, completed effects, remaining budgets, and the checkpoint sequence before a restart test.
• Reload the newest valid checkpoint and reject a future-version or corrupted checkpoint instead of guessing.
• Verify that resumption does not replay a committed effect. If exactly-once cannot be proven, downgrade the verdict and require reconciliation.

Parallel branches

Represent each branch as its own child attempt. The join record must retain success, failure, timeout, and not-started states. Choose one predeclared join policy:

• all_required: any required branch failure stops the join;
• best_effort: continue with an explicit degraded marker;
• compensate: run a bounded compensating action and then stop or resume.

Never let a successful sibling erase a failed branch from the final ledger.

Memory loss

Clear only the ephemeral context named in the schedule. Rebuild from the checkpoint and durable evidence, then check that the agent does not fabricate missing user intent, tool output, or approval. If a required fact is absent, the safe result is inconclusive or a human clarification state.