Reliability evidence for AI-agent startups

Your agent works. Prove how it fails.

EvidenceRun reviews one agent workflow against a 12-mode methodology and delivers a buyer-ready reliability report for investor, security, and enterprise conversations.

EvidenceRun dashboard showing run timeline, severity-flagged findings, tool-call evidence, and replay metadata for a sample agent workflow.
Dashboard view used during a pilot — findings are anchored to the run evidence that produced them.
Sample report View full report
Not observability Evidence packaging
Not certification Buyer readiness
Not generic governance Agent failure taxonomy

The deliverable

A report your buyer's security team can read.

Twelve failure modes mapped to trace evidence, with severity, remediation priority, and explicit limitations. Hand it to a buyer's diligence team with a concrete account of what was reviewed.

Agent Reliability Report Sample report Trace evidence, taxonomy mapping, and remediation backlog in one buyer-ready document.

The 12-mode taxonomy

What gets audited.

Every full audit reviews the same twelve modes. The local test audit surfaces eight preliminary signals; expert review covers the complete taxonomy, evidence gaps, and context that rules cannot decide.

Critical 4

Deal killers

  1. 03

    PII exposure

    Customer data, secrets, or prompt content leak to a third-party tool, log, or downstream model.

  2. 05

    Missing approval

    High-impact actions execute without the human-in-the-loop check the workflow promises.

  3. 11

    Unverifiable decisions

    A decision was made; nobody can reconstruct what the agent saw, asked, or weighed.

  4. 12

    No replay trail

    Inputs, prompts, model versions, and tool outputs are not stored long enough for an after-the-fact audit.

Operational 4

Ops nightmares

  1. 01

    Tool misuse

    Agent calls a tool with the wrong args, wrong scope, or no need to call it at all.

  2. 02

    Hidden retries

    Silent retry loops on non-idempotent calls cause duplicate side effects nobody can see in the trace.

  3. 06

    Runaway cost

    Recursive calls, retry storms, or context bloat send a single run past the alarm threshold.

  4. 08

    Silent failure

    Tool returned an error, the agent returned success — the user is told something happened that didn't.

Subtle 4

Long-tail risks

  1. 04

    Prompt injection

    Untrusted content in inputs, attachments, or web pages overrides system instructions.

  2. 07

    Stale context

    Agent acts on cached customer state, expired session data, or out-of-date documents.

  3. 09

    Wrong system access

    Agent inherits service-account permissions far beyond what the workflow requires.

  4. 10

    Output drift

    Customer-facing wording, format, or recommendations drift across runs in ways nobody noticed.

What gets audited

Three questions buyer reviews often start with.

Tool access

Which tools can the agent call, what arguments did it pass, and which calls should have required approval?

Data movement

Where did customer data, PII, secrets, prompts, and third-party API payloads move during the run?

Replay evidence

Can the team reconstruct what the agent saw, tried, changed, failed, retried, and claimed afterward?

Pilot offer

One workflow. Five business days. Fixed scope.

1

Pick one agent workflow that matters for sales, support, finance, coding, research, or operations.

2

Share traces, logs, prompts, tool calls, screenshots, or run a short screen-share walkthrough.

3

Receive an Agent Reliability Report: failure taxonomy mapping, evidence examples, and remediation backlog.

3 pilots at $1,000 each. 5-day delivery.

For startups building AI agents that need to look serious in front of investors, security teams, or enterprise buyers.

matvei@evidencerun.com
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EvidenceRun reports are reliability evidence for product, security, and investor conversations. Not a legal compliance certification or safety guarantee.