Context failure
Missing, stale, contradictory, malicious, or over-broad context changes the decision.
AI agent reliability audit
Test the workflow, not only the agent's final answer.
AI agent reliability depends on the whole execution path: context, planning, tool permissions, external systems, retries, state, human authority, and the final output.
01 / Draw the action boundary
Start with what the agent can observe and change.
List every data source, tool, credential, write operation, external dependency, approval point, and persistent state. Mark the actions that are reversible, the actions that require review, and the actions the agent must never take.
Engineering reasoning: the same answer has different consequences when it is displayed as a draft, sent to a customer, written to a system of record, or used to trigger another automated action.
02 / Build the failure model
Test where control can be lost.
Tool failure
Timeouts, partial writes, duplicate calls, changed schemas, and permission errors produce ambiguous state.
Planning failure
The agent chooses the wrong action, repeats a step, skips a required check, or continues after evidence is insufficient.
Control failure
A review gate, allowlist, spending limit, escalation rule, or rollback path does not hold under the real execution path.
03 / Capture the trace
Make a failed run reproducible without exposing more data than necessary.
Record the versioned instructions, model and tool configuration, allowed inputs, tool requests and outcomes, control decisions, final status, latency, cost, and correlation identifiers. Apply the same access, retention, and redaction rules used for other consequential system logs.
Evaluate both the trace and the result. A plausible final answer does not make an unauthorized or unsafe intermediate action acceptable.
Reliability is the ability to keep the action inside its boundary, detect when it leaves, and recover deliberately.
04 / Run the audit
A practical AI agent reliability audit
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Define intended actions and prohibited actions
Assign an accountable owner and document the consequences of an incorrect action.
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Replay representative and adversarial cases
Include tool errors, malformed data, stale state, repeated requests, and attempts to override instructions.
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Repeat non-deterministic cases
Measure the distribution of outcomes and the frequency of escalation rather than recording one favourable run.
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Exercise human authority and rollback
Confirm that reviewers receive enough evidence, blocked actions stay blocked, and recovery works after partial execution.
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Set release and monitoring gates
Separate failures that block release from events that can be monitored with a named response owner.
Public sources
Sources that inform the controls
Google SRE: Monitoring Distributed Systems
The SRE chapter distinguishes internal metrics from tests of externally visible behaviour and explains alerting around actionable failures. Read the SRE chapter.
OWASP Top 10 for LLM Applications
OWASP's application-risk framing supports testing the inputs, outputs, permissions, and downstream actions around the model. Read the OWASP project.
Published method
Agent frameworks for this decision
For agent-based workflows, these documents expand on the decision above. They describe our method, not client results.
AI Agent Control Checklists
Turn the action boundary and failure cases into control inspections with explicit pass conditions.
AI Agent Security Threat Framework
Extend the reliability audit with abuse cases for instructions, data, tools, identity and state.
Next decision
Scope the evaluation problem
Describe the system and the failure you need the evaluation harness to catch. Alpha AI will use this context to frame a bounded engineering conversation.
We review your enquiry and reply by email. No commitment to a paid project; scope and terms are agreed before work begins.
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