# From Agent Evals to Glass-Box Enterprise AI

*By PM Daily Digest • August 1, 2026*

Supabase’s agent benchmark and Decagon’s productized deployment model point to a new PM bar: measure real workflows, turn implementation into reusable product, and preserve human taste.

## Big Ideas

**Agent compatibility is moving from claim to test.** Supabase introduced Evals, running Claude Code, Codex, and Open Code against real tasks and scoring what they do. [^1] Paul Graham says he expects all services used by agents eventually to adopt such tests—and that services agents cannot use could go out of business. [^2] For PMs, define “agent-ready” through representative workflows and measurable success criteria; turn failed runs into product priorities.

**Fast AI cycles favor stable direction over detailed schedules.** Decagon says a precise 12-month roadmap is difficult when build cycles are so fast; it keeps themes and a clear long-term vision, lets customer signals determine what to build, and retains human judgment over what to include or exclude. [^3] Apply this as “stable vision, short commitment horizons”: make the current bet explicit, keep later bets provisional, and review them against fresh customer evidence.

## Tactical Playbook

**Make feedback earn a roadmap slot.** A community practitioner’s process is: stop accepting feature requests and capture the problem or symptom; quarterly observe 10–15 users in their normal environment; collect about 15 pains; have 100–150 users rank them; prioritize with a weighted average; and reserve roughly one-third of sprint capacity for customer-satisfaction fixes, leaving two-thirds for strategy and technical debt. [^4] Use those figures as a starting hypothesis, not a law. At minimum, track each theme’s source, segment, observed behavior, frequency, severity, and blocked business outcome; promote it only when evidence is strong, and link the roadmap item to the evidence and to what would change your mind. [^5]

**Separate “Now” from discovery.** One startup stopped weekly roadmap churn by keeping Now stable except for critical or regulatory items while allowing Next and Later to change; the commenter says the approach held through acquisition and scale-up. [^6] The organizational prerequisite is role clarity: define what PM owns before hiring, and keep early teams on high-trust, light rituals rather than importing frameworks and OKR cascades too soon. [^7]

## Case Studies & Lessons

**Decagon’s “glass box” turns deployment into product.** Its forward-deployed teams are expected to contribute to core product, so a capability built for one enterprise becomes available to the next 10 customers rather than remaining one-off work. [^3] In a customer comparison, a Sierra deployment produced about three new journeys over a year because the customer depended on forward-deployed engineers; with Decagon’s productized model, the same customer created about seven in a month, with nontechnical teams able to act directly. [^3] Lesson: treat recurring implementation labor as product debt. Instrument what specialists repeatedly do, generalize it, and give customers enough control to iterate without waiting on the vendor.

## Career Corner

**PM interviews are becoming build tests.** The discussion describes companies replacing presentation rounds with live prototypes to assess “full stack builders.” [^8] Evaluators look for taste and decision ownership—not a prototype where AI made all the choices—and flag generic copy, weak underlying data, and designs that feel like AI output. [^8] Prepare by giving the tool context in three buckets—functionality, design, and data—then attach a wireframe and realistic dataset; add a live API and be ready to explain front/back-end boundaries, security, model choice, and caching. [^8]

## Tools & Resources

**Choose the prototype stack by the job.** The current tool map separates design-system/front-end tools (Reforge Build, Magic Patterns, Alloy), full-stack zero-to-one tools (Lovable, Bolt, Replit), and full AI development tools (Claude Code, Codex). [^8] For interview work, the recommended differentiators are visual context, thoughtful copy and data, and a working API—not faster generic generation. [^8]

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### Sources

[^1]: [𝕏 post by @supabase](https://x.com/supabase/status/2083282155170340898)
[^2]: [𝕏 post by @paulg](https://x.com/paulg/status/2083289602127716384)
[^3]: [Decagon’s Playbook for Building Enterprise AI Applications](https://www.youtube.com/watch?v=cO1f2wOxSH4)
[^4]: [r/ProductManagement comment by u/GeorgeHarter](https://www.reddit.com/r/ProductManagement/comments/1vbqvwf/comment/p0vn9mw/)
[^5]: [r/ProductManagement comment by u/coolreddy](https://www.reddit.com/r/ProductManagement/comments/1vbqvwf/comment/p0xfe25/)
[^6]: [r/ProductManagement comment by u/LakeHold](https://www.reddit.com/r/ProductManagement/comments/1vbso6g/comment/p0xm59r/)
[^7]: [r/ProductManagement comment by u/rash3rr](https://www.reddit.com/r/ProductManagement/comments/1vc0r45/comment/p0xr1ue/)
[^8]: [Prototyping in Interviews | Theory & Practice](https://www.youtube.com/watch?v=G_Ek-NOpAuQ)