How to Evaluate Agent Team Output Quality

The Evaluation Problem

Single-agent outputs are easy to judge. You read the response and decide if it's helpful. Agent team outputs are harder — you're evaluating a multi-section deliverable that spans several domains. Where do you start?

This framework gives you a systematic way to evaluate agent team quality across four dimensions.

Dimension 1: Depth

What to check: Does each section go beyond surface-level observations? Are there specific data points, concrete examples, and nuanced analysis — or just generic statements?

Red flags:

• Vague quantifiers: "significant market share," "growing rapidly," "many customers"
• Missing specifics: no numbers, no names, no concrete examples
• Repetitive points restated in different words

What good looks like:

• Specific metrics: "estimated $4.2B TAM growing at 18% CAGR"
• Named examples: "Notion's shift to enterprise pricing in Q3 2025"
• Layered analysis: observation → evidence → implication

Quick test: Pick any claim in the output. Can you act on it without additional research? If the answer is consistently no, the depth is insufficient.

Dimension 2: Accuracy

What to check: Are the facts and claims reliable? Agent teams can produce confident-sounding analysis built on hallucinated data.

Red flags:

• Specific numbers that seem too precise or too convenient
• Claims about recent events that don't match your knowledge
• Statistics without clear methodology or source context

What good looks like:

• Claims qualified with confidence levels: "publicly reported revenue of $50M" vs. "estimated revenue of $40-60M based on employee count and typical SaaS metrics"
• Explicit acknowledgment of information gaps
• Internally consistent numbers across sections

Quick test: Spot-check 3-5 specific factual claims against sources you trust. If more than one is wrong, the output needs prompt refinement.

Dimension 3: Coherence

What to check: Does the deliverable read as a unified document or as disconnected sections stapled together? Does the synthesis actually synthesize?

Red flags:

• Contradictions between sections (one agent says the market is growing, another's analysis assumes decline)
• No cross-references between sections
• Executive summary that doesn't reflect the detail sections
• Abrupt tone or style shifts between sections

What good looks like:

• Synthesis section references and reconciles findings from individual agents
• Consistent terminology across all sections
• Logical flow from analysis to recommendations
• Contradictions explicitly acknowledged and addressed

Quick test: Read only the executive summary, then read the full output. Does the summary accurately represent the key findings? If not, the synthesis is weak.

Dimension 4: Actionability

What to check: Can a decision-maker use this output to make a specific decision or take a concrete next step?

Red flags:

• Recommendations that are too generic: "invest in marketing" or "improve customer experience"
• Analysis without implications: data presented but never connected to "so what?"
• Missing prioritization: everything presented as equally important

What good looks like:

• Specific, prioritized recommendations: "Focus Q2 budget on mid-market segment ($50-200 employees) where competitor X has weakest positioning"
• Clear next steps with owners and timelines
• Trade-offs explicitly stated: "Option A is faster but riskier; Option B is safer but requires more investment"

Quick test: After reading, can you list 3 specific actions to take? If not, the output isn't actionable enough.

The Evaluation Checklist

Use this after every agent team run:

• Depth: Each section contains specific data, examples, and layered analysis
• Accuracy: Spot-checked 3-5 claims; no significant errors found
• Coherence: Sections connect logically; synthesis resolves contradictions
• Actionability: Output leads to specific, prioritized next steps

Improving Quality Over Time

When evaluation reveals gaps, the fix is almost always in the prompts:

• Shallow depth → Add specificity requirements: "Include at least 3 specific data points per competitor"
• Accuracy issues → Add hedging instructions: "Clearly distinguish between verified facts and estimates"
• Poor coherence → Strengthen the synthesis prompt: "Identify and resolve any contradictions between agent outputs"
• Low actionability → Add output requirements: "End with 3 prioritized recommendations, each with a specific next step"

The evaluation framework isn't just a quality gate — it's a feedback loop that makes your agent teams better with every iteration.

Build a team and test the framework →