# Build products agents can call: Cisco and Vercel on designing for agent users

*By PM Daily Digest • November 5, 2026*

Cisco and Vercel executives argue products now have to work for agents as well as people. Also: Jenny Martin's Oopsie framework for connecting discovery and delivery, Evan Campbell on strategy funding, and PMs debating what AI really costs them.

## Your next user may be an agent

Executives at Cisco and Vercel made the same argument from different directions: products now have to work for agents, not just people.

Vercel's COO put it bluntly. "Agents don't live in UIs. They call APIs. they hit MCP servers." A product that isn't composable and open to developers is "invisible to agentic workflows" [^1]. His advice is to build developer surfaces now: MCP servers, webhooks and APIs [^1]. He also said agents built without a structured, queryable model of the business "hallucinate or give generic answers" [^1].

Vercel's own build process is worth borrowing:
- A GTM engineer shadowed a top SDR and turned each tool she used into a workflow step before any AI was involved [^1].
- The agent then ran in shadow mode for six weeks. The SDR reviewed every output until she couldn't improve it [^1].
- The lead-qualification agent costs under $5,000 a year plus about 20% of one engineer's time [^1].
- Every quarter, the team checks whether a vendor has built something better, faster or cheaper [^1].

Cisco's Jeetu Patel (President & CPO) said Cisco just launched a personal concierge agent that brands can give every customer, not just the top 20 or 100 accounts that get named reps [^2]. He also explained the shift that set this up. After 250 acquisitions, Cisco worked like a holding company and has now become a platform company. "We don't have any product managers at Cisco. We have feature managers," building features for the platform [^2]. Two more lessons from him:
- A second product that doesn't use the first product's route to market will fail [^2].
- When a market looks important, Cisco now asks "what are we going to build" instead of "what are we going to buy" [^2].

On judgment, Patel said: "what you choose not to build is far more important than what you choose to build," and that choice takes taste and an instinctive grasp of the market and customers [^2].

There's a trust problem underneath all this. In a16z's review of about 3,600 messages from AI power-user group chats, trust was the top fear, raised in 22% of them [^3]. Their suggested design pattern: scoped permissions and monitoring, with the agent doing the groundwork and the user approving the final decision and its cost [^3].

## Connecting discovery to delivery

On Mind the Product, Jenny Martin explained Oopsie, a framework with five layers: outcomes, outputs, process, scenarios and inputs [^4]. The idea is to do enough thinking to build the right thing without falling into waterfall-style analysis paralysis [^4].

- **Outcomes.** An outcome is a real-world change that creates value, usually for a customer. "A number on a spreadsheet is not the outcome" [^4].
- **Outputs.** Each layer is a chance to prioritize. She estimates that about 20% of possible outputs deliver about 80% of the value [^4].
- **AI features.** Non-deterministic behavior needs evals with ranges and scoring. Deterministic results need exact expected answers [^4].

Her example: she once showed a retail customer the exact test data the team planned to use. The customer spotted a fundamental wrong assumption, which saved an estimated three or four months of work [^4].

## Funding strategy, not projects

Evan Campbell (One Knight in Product) said the most common way OKR programs fail is tying them to rewards, because people then sandbag their objectives [^5]. His portfolio approach:
- Rank cost-cutting work by NPV or IRR, but judge innovation only against strategic objectives. "There is no ROI" calculation for that work [^5].
- Set aside a share of investment, such as 10%, for innovation so it doesn't get starved [^5].
- Fund work in stages, and "trim the tail" once another quarter of investment would return less than a new idea would [^5].

## Cheap copying and the SaaS question

Casey Winters argues that AI makes copying so cheap that competitors "copy first and find out later." Copying is becoming a better tactic, he says, but not necessarily a better strategy [^6].

On r/ProductManagement, a claim that one CEO used Claude to replace 9 SaaS vendors and raise margins 25% [^7] drew pushback. One reply said he built 9 good-enough internal tools, not 9 products, and now has to maintain them [^8]. Another said vendors take on risk for compliance, security and support [^9].

Hiten Shah offered a more modest opportunity. Coding agents make the "unwritten backlog" of changes people stopped requesting worth revisiting [^10].

## Where AI costs PMs time

A widely discussed thread listed the costs:
- **Slop docs.** A 20-page AI-written requirements doc where a paragraph would do [^11].
- **Surprise errors.** Claude helps one PM 95% of the time, but random hallucinations cut the net gain to about 50% [^11].
- **Hard-to-trace PRDs.** Generated PRDs make it hard to tell where KPIs came from [^12].
- **Always-on recording.** AI recording every call and Slack message makes search easy, but leaves a record of everything anyone says [^11].

## Status reports you can't trust

For a PM whose dev team keeps reporting false progress, the most practical replies:
- Require a working demo on shared staging by day 7 of the sprint.
- Log committed date, delivered date and first-try acceptance-criteria pass rate, then bring three sprints of trend data to whoever owns delivery [^13].

Several commenters said accountability for estimates and delivery belongs with the engineering manager [^14].

## Also

Conductor, the AI coding app, is joining Meta to build "the next interface" with Meta Superintelligence Labs (MSL) [^15].

---

### Sources

[^1]: [Going from 10 to 1 SDRs. How We Deployed AI Across our Sales Org with Vercel's COO](https://www.youtube.com/watch?v=wXHLbr4W-8s)
[^2]: [Cisco President & CPO on Rebuilding Products So Agents Can Use Them | Jeetu Patel](https://www.youtube.com/watch?v=ilLl36NGreU)
[^3]: [The New AI Agents That Shop, Plan, and Gossip](https://www.youtube.com/watch?v=dSiTwPl0t1U)
[^4]: [How to close the gap between discovery and delivery | Jenny Martin](https://www.youtube.com/watch?v=lNvisfQM8v0)
[^5]: [Evan Campbell: Your Strategy Is Decaying... Here's How to Save It](https://www.youtube.com/watch?v=EbaHgSXA4Q0)
[^6]: [𝕏 post by @onecaseman](https://x.com/onecaseman/status/2118026648616575208)
[^7]: [r/ProductManagement comment by u/Randombu](https://www.reddit.com/r/ProductManagement/comments/1xo4k0r/comment/19s5lgwlbfc5c/)
[^8]: [r/ProductManagement comment by u/Bernhard-Welzel](https://www.reddit.com/r/ProductManagement/comments/1xo4k0r/comment/19s5r27fvsfut/)
[^9]: [r/ProductManagement comment by u/Buffett_Goes_OTM](https://www.reddit.com/r/ProductManagement/comments/1xo4k0r/comment/19s5p533unyv4/)
[^10]: [𝕏 post by @hnshah](https://x.com/hnshah/status/2117996540799410578)
[^11]: [r/ProductManagement post by u/GreenLeftPlank](https://www.reddit.com/r/ProductManagement/comments/1xnnxv7/)
[^12]: [r/ProductManagement comment by u/yourlicorceismine](https://www.reddit.com/r/ProductManagement/comments/1xnnxv7/comment/19s4r4pkmjh0g/)
[^13]: [r/ProductManagement comment by u/Acceptable-Run-6542](https://www.reddit.com/r/ProductManagement/comments/1xnrlfl/comment/19s59hh305bnp/)
[^14]: [r/ProductManagement comment by u/familiar_novel](https://www.reddit.com/r/ProductManagement/comments/1xnrlfl/comment/19s4vd0pd516q/)
[^15]: [𝕏 post by @charlieholtz](https://x.com/charlieholtz/status/2118045232218743264)