The AI PM Skill That Gets You Instant Job Offers | Aparna, Arize AI

Aparna Dhinakaran, CPO of Arize AI ($131M raised), shows exactly how to build a PM agent in Claude Code, instrument it with observability, run evals against it, and close the self-improvement loop, all in one live session. If you want to understand what serious AI eval practice looks like in 2025, this is the episode.

Full Writeup: https://www.news.aakashg.com/p/aparna-dhinakaran-podcast
Transcript: https://www.aakashg.com/how-to-build-a-self-improving-pm-agent-in-claude-code/

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Timestamps:

00:00 - Intro
01:34 - What PMs get wrong
04:35 - Why product taste is the alpha
06:30 - The build-trace-eval-loop framework
08:04 - Ads
09:28 - Building the agent in Claude Code
19:00 - Instrumentation in one command
22:00 - Traces streaming into Arize live
28:00 - Asking Claude to suggest evals
31:13 - Ads
33:58 - Vibe evals vs axial coding
48:50 - Looping the improvement automatically
01:06:00 - The context graph unlock
01:18:55 - Outro

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Key Takeaways:

1. Trace before you eval - A trace is the full step-by-step playback of what your agent did. Without it, you have no evidence base for evals. Every LLM call, every tool call, every intermediate output needs to be visible before you write a single eval.

2. A span is your unit of evaluation - A span is one discrete step inside a trace. Evals run at the span level, not the trace level. "Did this specific scoring step get the priority right?" is a more useful question than "was the whole run good?"

3. Instrumentation is now a one-command job - Claude Code's instrumentation skills can set up observability for your agent automatically. Arize Phoenix's skill looks at your codebase, identifies the LLM calls and tool calls, and wires them to the tracing layer. No engineering support required.

4. The vibe eval is a draft, not a verdict - An LLM can suggest what your evals should test by looking at your traces. That suggestion will not know your bug-first policy, your comp logic, or your definition of "critical." Treat it as v0 and refine against your actual judgment.

5. When evals fire, two things could be wrong - The agent produced a bad output. Or the eval is miscalibrated. Reading the flagged span yourself is the only way to know which one needs fixing. Both are normal. Both are good news.

6. Evals drift and need regular realignment - Your priorities change. Your bug policy changes. Your product changes. An eval calibrated to last quarter will start misfiring this quarter. Regular alignment to human feedback is maintenance, not a failure.

7. The self-improvement loop is already running at the best teams - Fetch all spans where evals fired. Group by failure category. Propose a specific prompt fix. Review and approve. Ship the new version. This loop runs on a schedule and requires a human at the approval step.

8. Enterprise PMs: start with one internal agent - Not a customer-facing product. An internal tool that takes four hours off your week. Once you have it, you will naturally want to trace it. That is when observability starts to matter to you personally.

9. The context graph is the enterprise unlock - Agents are only as useful as the context they have. Enterprise data lives in silos. The teams breaking through are building unified context layers that give one agent access to CRM, Gong, analytics, GitHub, and Slack.

10. Product taste is still the alpha - Code is cheap now. Shipping speed is table stakes. The PMs who pull ahead are the ones with the sharpest judgment about what to build, and the loops that make their agents better every day.

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Where to find Aparna Dhinakaran:
LinkedIn: https://www.linkedin.com/in/aparnadhinakaran
Arize AI: https://arize.com

Where to find Aakash:
X: https://x.com/aakashgupta
LinkedIn: https://www.linkedin.com/in/aagupta/
Newsletter: https://www.news.aakashg.com

#AIagents #ProductManagement

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Видео The AI PM Skill That Gets You Instant Job Offers | Aparna, Arize AI канала Aakash Gupta