Rubricon: How Agentic LLMs Are Changing Product Evaluation

A Shift in How We Evaluate Product Ideas

Product evaluation has always been messy. Frameworks exist, but applying them consistently is time-consuming, subjective, and often limited to a few “big bet” ideas. Smaller, incremental ideas get lost.

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What’s different now? The rise of agentic LLMs — models that don’t just generate text, but can fetch data, apply structured reasoning, and produce actionable outputs. Rubricon harnesses this capability to transform product evaluation from a manual, biased process into a scalable, transparent, and repeatable system.

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The Rubricon Tech Stack

At its heart, Rubricon is not just a rubric — it’s a workflow powered by AI agents.

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1. Input Integration

• Rubricon agents connect directly to systems like Jira.
• They fetch the idea description, labels, components, and linked issues automatically.

2. Rubric Application (UMSF)

• The UMSF rubric defines how ideas should be assessed (market, adoption, technical effort, etc.).
• This rubric is machine-readable, enabling the LLM to apply it consistently.

3. LLM Agent Reasoning

• The agent LLM doesn’t just “score” ideas; it reasons through each rubric dimension.
• It produces structured scores (1–5), an overall 0–100 score, a priority band, and narrative justifications.

4. Structured Output

• Results aren’t raw text — they’re rich JSON objects that include opportunities, risks, dependencies, and next actions.
• This makes them easy to route into dashboards, decision workflows, or even back into Jira comments.

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Why This Is a Step Change

Previous attempts at scoring ideas struggled with scale and bias. Rubricon’s agentic architecture enables a fundamental shift:

• Consistency by Design: Every idea is evaluated against the same rubric, with the LLM enforcing the structure.
• Scale with Confidence: Dozens or hundreds of ideas can be scored overnight, not weeks.
• Machine + Human Synergy: Humans still make final calls — but now they start with a clear, structured baseline.
• Extensible: Because the output is structured, Rubricon can plug into existing roadmapping tools, dashboards, or prioritization frameworks.

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Example Flow (Simplified)

1. Agent fetches Jira issue IPI-19

2. Agent fetches UMSF rubric (v1.0)

3. Agent LLM applies rubric → scores + justifications

4. Agent outputs structured JSON with score, band, risks, opportunities

5. Workflow posts summary back into Jira

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This loop is the engine that makes Rubricon practical, scalable, and trustworthy.

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What This Means for Teams

With Rubricon, evaluation is no longer:

• A once-a-quarter ritual → it can be continuous.
• A debate about bias → it’s anchored in a consistent rubric.
• A burden on PMs → much of the heavy lifting is handled by the agent LLM.

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Instead, teams can focus on strategic discussion, not whether someone applied the rubric correctly.

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Looking Ahead

As agentic LLMs evolve, Rubricon will gain:

• Dynamic rubrics that adapt based on historical outcomes.
• What-if analysis (e.g., “double the weight of adoption — how does the pipeline reorder?”).
• Cross-organizational benchmarking (compare scoring patterns across business units).

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This isn’t just tooling — it’s a new discipline. Product evaluation as code.

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Final Thought

Agentic LLMs mark a turning point in how we evaluate product features. With Rubricon, we’ve crossed the threshold — from subjective debate to structured, AI-augmented decision-making.

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Next: we’ll show how the UMSF rubric is encoded for agent reasoning and what a sample evaluation looks like end-to-end.

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CALLOUT: Sample Rubricon Output (Simplified)

{ "issueKey": "IPI-19", "issueSummary": "Add in-app referral rewards", "type": "B", "overall_score_100": 74, "priority_band": "Medium", "rubric_version": "1.0", "scores": { "M_market": 4, "A_adoption": 4, "T_effort": 2, "S_strategy": 3, "R_risk": 2 }, "opportunities": [ "Drives organic growth via referrals", "Increases engagement without ad spend" ], "risks": [ "Engineering complexity for fraud detection", "Requires marketing ops support" ], "dependencies": [ "Payments team", "Fraud detection API" ], "next_actions": [ "Run quick technical feasibility spike", "Validate reward mechanics with 10 pilot users" ], "confidence_0_1": 0.7 }