Know it's good, not just done
An agent always returns something. The hard part is knowing whether it is any good. Evals score every output against a rubric you define, so you get a number and a reason, not a gut feeling. Trust the work without re-reading every line.
against your rubric
quality over time
a reason, not a verdict
"Done" tells you nothing about good
An agent will always hand you a finished-looking output. Whether it meets the bar is the question it cannot answer about itself, so the judgment falls back on you, every single time.
You re-read every word anyway
The agent saved you the writing, but you still proofread the whole thing to make sure it holds up. The review is now the slow part.
Good is a gut call
Was that PRD solid, or did it just look polished? Without a standard to check against, quality comes down to how you felt reading it.
Quality slips without warning
A prompt tweak or a model change makes outputs worse without anyone noticing, and you find out when a stakeholder points at the weak one.
A rubric that turns taste into a score
You define what good means in criteria and weights. Evals apply that same standard to every output, so quality stops depending on whether you were paying close attention that day. For stopping bad output before it ships, see guardrails.
Every required section is present
A new reader follows it without help
Matches the goal you set
Empty, error, and abuse states named
Your standard, applied consistently
The rubric is yours. Start from the default each agent provides, then tune it until the score matches what you would have said in a review. After that, every output gets the same fair read.
- Score against the criteria you care about
- Weight what matters more for this kind of work
- Each agent ships with a sensible default rubric
- Every score comes with a short reason you can read
Four steps, running in the background
You set the rubric once. After that, scoring happens on every output without you asking, and the history builds itself.
Define the rubric
Pick the criteria that matter and what good looks like for each. Start from the agent default and adjust.
Score every run
On each output, every criterion gets a score and a short reason, so the result is explainable, not a black box.
Roll up to a result
Scores combine by your weights into one overall number, with a clear pass or needs-work against your bar.
Track over time
Every score is kept, so you can watch quality trend across runs and get flagged the moment it drops.
Catch the dip before a stakeholder does
Because every run is scored and kept, a drop in quality shows up as a dip on the chart, not as a surprise in a review. When a change makes outputs worse, the eval flags it while you can still fix it.
Questions about evals
What pairs with evals
Guardrails
Evals score how good an output is. Guardrails stop output that breaks your rules. Use both to ship work that is safe and strong.
Learn more →Sub-agents
A multi-perspective review and an eval score answer different questions: what to fix, and how close to the bar you are.
Learn more →Memory
When an eval flags a recurring miss, memory helps the agent avoid it next time, so scores climb instead of plateau.
Learn more →AI PRD Writer
Every PRD it drafts gets scored against a rubric, so you know the draft is strong before you spend time reviewing it.
Learn more →