Your agent needs a quality gate — here's how to catch bad output before it ships

Your agent just spent 20 minutes writing a detailed analysis of your quarterly metrics. It's well-formatted, sounds authoritative, and is completely wrong because it misread a column header. You don't catch it until you're presenting to your board.

This is the agent quality problem: they're getting better at producing confident garbage. The solution isn't hoping for perfect models — it's building quality gates that catch problems before they matter.

The simplest quality gate: the verification prompt

After your agent completes any substantive task, run its output through a verification step:

Review this analysis for accuracy:

[AGENT OUTPUT]

Specifically check:
- Are the numbers pulled from the right data sources?
- Do the calculations add up?
- Are there any obvious logical inconsistencies?
- Does this pass a basic sanity check?

Return: PASS, FAIL, or NEEDS_REVIEW with specific issues.

This catches about 70% of quality problems. The agent reviews its own work with fresh context and different instructions.

The rework rate pattern

Track how often your agent's work needs revision. If you're constantly fixing output, that's data:

• Rework rate above 30%? Your instructions are unclear
• Same types of errors repeating? Add those specific checks to your quality gate
• Perfect output but takes forever? Your quality gate is too strict

I keep a simple log: task type, initial quality (1-5), and what needed fixing. Patterns emerge fast.

The confidence calibration trick

Ask your agent to rate its confidence in the output (1-10). Anything below 7 goes to a human reviewer automatically. Agents are surprisingly good at knowing when they're guessing.

Pro tip: Use a different model for verification. GPT-4 catches Claude's mistakes better than Claude catches its own, and vice versa. Cross-model review is like having a second pair of eyes.

The output fingerprint

Bad agent output has tells. Watch for:

• Hedge words everywhere ("might", "could", "potentially")
• Suspiciously round numbers
• Generic examples that don't match your context
• Conclusions that don't follow from the data presented

Build these checks into your quality gate. If the output trips multiple tells, flag it for review.

The human-in-the-loop escape hatch

Some work is too important for any automated quality gate. Define your stakes clearly:

• Financial analysis over $10K? Always human review.
• External communications? Human approval required.
• Code that touches production? Verification loop mandatory.

Your agent should know these boundaries and route accordingly.

Quality gates aren't about perfection — they're about catching expensive mistakes before they compound. Start with verification prompts, track your rework rate, and build from there.