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Ten questions agent developers ask when their agents start failing confidently. Each answered with our own operating data. No vendor claims, no affiliate links.

production calibration curve 0.914 realized accuracy (n=116)

Verifying outputs

All verification capabilities are measured against our production calibration curve — 0.914 realized accuracy (n=116).

• How do I verify my AI agent's outputs?

  The foundational guide: external, adversarial verification with a measured calibration curve — because self-checking doesn't work.

• Why does my agent hallucinate citations, and how do I catch it automatically?

  Citation hallucination is a trust killer; here's how to catch it automatically in your pipeline.

• Agent QA patterns: test suites for non-deterministic systems

  Testing agents is different from testing code; these patterns handle the nondeterminism.

Building for agents

The pieces agents need to act, pay, and be auditable.

• What is x402 and how do agents pay for API calls? coming

  The HTTP payment standard that lets agents buy verification (and other APIs) themselves.

• Tamper-evident receipts: proving what your agent did and when coming

  Signed, hash-chained receipts that make agent actions auditable after the fact — receipts you can't quietly rewrite.

• How do I make my site readable by AI agents? coming

  The free scanner shows you what agents see when they visit your site.

• llms.txt, schema.org, agent cards: what actually matters in 2026 coming

  Which machine-readable standards earn agent traffic, and which are noise.

Calibration and measurement

Confidence without a measured curve is a vibe.

• Calibration for LLM verdicts: why "confident" must mean "measured" coming

  A confidence score without a measured curve is a vibe.

Proving and isolating

When the agent needs to prove it didn’t leak.

• The isolation problem: proving your AI service can't leak its operator's data coming

  How to demonstrate that your agent's outputs can't expose your data.

Adversarial verification

Because agents can’t mark their own homework.

• Adversarial verification vs. self-grading: why agents can't mark their own homework coming

  A model grading its own answer inherits its own blind spots.

See what agents see on your own site with the free scanner. No signup.

Unpublished pages ship one at a time — the verification engine is live for those who can't wait.