Mike Durst

Agents ≠ workflows. The real leverage is the hybrid: agents for judgment, flows for precision. We just rolled out Skillful Flows alongside our fully agentic stack—built for control, auditability, and speed. Read the piece. Then build your first Agent–Flow in https://app.skillfulai.io/

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More proof assets do not equal credibility. When proof is thin, don't make loud claims. Use a repeatable way to prove what you’re saying. The failure mode is simple. Startups makes claims without evidence and buyers can see the guess work As a PMM fix it with stacked proof One layer per core claim and standardizes it- claim → proof → measurement. Level 1: Mechanism proof Share screen flow, workflow diagram, demo steps that customers can relate to Level 2: Workflow proof Show steps removed, steps added, handoffs changed in the customer's process Level 3: Early signal proof Activation, time-to-first-value, usage frequency, retention trend, pilot completion. Level 4: Outcome proof Show potential win rate, conversion, cycle time, costs saved, revenue impact. Only if you can attribute it. Operating rule: Do not publish a claim above your highest proof level. If you don’t have outcomes, you still have options. Run a pilot to validate what is sticking and that can be the proof point Define the pilot user and workflow. Define one success metric tied to the workflow. Define one adoption metric tied to usage. Define the time window. Show what “good” looks like. At Awvent when we had no customer we applied this process with countless potential customer..refined it with every feedback...eventually landing our first customer ( always a sweet feeling) As early stages startup this is one way you stay credible while the product is still earning outcomes.

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The hottest role in AI startups right now isn’t Forward Deployed Engineers. It isn't GTM Engineers. It’s Deployment Strategists. Decagon calls it an “Agent Product Manager.” Harvey calls it a “Solutions Architect.” Palantir Technologies has had versions of this role for years. And the salaries are climbing fast: - Decagon: $200k–$285k - Palantir Technologies: $120k–$200k - Figma: $150k–$260k - Ramp: $100k–$180k - Harvey: $190k–$260k So who are these people? They are usually pseudo-technical -- CS or engineering majors, or folks with technical work experience. Many come from 2 years in consulting, IB, or PE, then jump into startups to get their hands dirty. They are young, hungry, polished, and comfortable being in front of customers. What do they actually do? They make sure enterprise AI deployments succeed. A $100k+ deal does not survive on a nice pitch or a self-serve onboarding flow. It survives if the customer sees value in the pilot. That means: - Embedding directly with the customer - Designing prompt logic for specific workflows - Working with engineering to align integrations and data flow - Helping exec teams define their AI roadmap - Running feedback loops into product and GTM Why does this role matter so much? Because enterprise AI is messy. Integrations, data transfer, and adoption make or break a deal. Most buyers are using AI for the first time, and each has unique workflows. Deployment Strategists bridge that gap. They own the outcome. They are accountable for making pilots successful, which often means millions in revenue down the line. At Conversion, Sam Bochner has been leading this work for us. We are now thinking about scaling it into a full team. Because a few successful pilots can fund an entire department, and the cost of failed deployments is too high to ignore. Is this just a rebrand of customer success? Not really. Success is about answering tickets and renewals. Deployment Strategy is about going deep with a few enterprise accounts, extracting maximum value, and ensuring the pilot closes into a multi-year contract. Call it Agent PM, Solutions Architect, or Deployment Strategist. Whatever the title, this is becoming one of the most important roles in AI SaaS.

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My two cents on the Claude Squeeze Application layer AI companies being built right now will not survive if they incorporate human in the loop as a core part of how work gets done. If your agent requires a human to approve the output, you’re building a better UI for Claude. If your agent helps an accountant do more accounting or a lawyer do more lawyer-ing, you might have a business, but companies won’t pay a lot for it. It’s not that Claude is going to steal your customers. It’s that the delta between what you’re offering and what someone gets from just using Claude directly is getting smaller every month. The complexity you’re solving for, better UX, smarter prompts, cleaner folder structures, is real, but it’s not worth a big check. And buyers are figuring that out. I’m already seeing it. A real estate brokerage looking at AI underwriting tools realized they can dump a rent roll straight into Claude and get 80% of the output for free. They’ll pay a little more for a better interface, but not a lot more. If you’re not doing the job end-to-end, your value is the productivity layer. And Claude is coming for the productivity layer. Hot take: I don’t think Harvey would get funded in 2026. They were funded when they were because the models weren’t advanced enough to reveal how thoroughly they’d eat the application layer. Harvey may survive, they’ve built real brand and enterprise relationships. But with that exact pitch today I think you’d struggle. Here’s what I think survives: Agents that own the outcome. They have to clear every one of these bars: 1. Fully autonomous. No human in the loop. The agent does the job, not assists with it. 2. Accountable for outcomes. Not activity. Results, and the agent is measured on that basis. 3. Mission-critical metrics. The outcomes it owns show up in the Monday morning operating review. The numbers the CEO reports. Going back to Harvey, if they said we’ll just do your legal work for you, end-to-end, accountable to outcomes, that’d be a defensible business in 2026. But notice what it is: outsourced labor with AI as the delivery mechanism. Not a productivity tool. A law firm that doesn’t charge law firm rates. Most AI companies won’t make that leap because it means owning outcomes. It’s hard. That’s what we’re building at Uniti. If you can autonomously run the entire leasing funnel for a property manager and own their rentals-per-month and conversion rate, you’re not a software vendor. Then do the same for all their workflows. You’re outsourced labor. Completely different moat. The mistake is selling the tool when they should be doing the job. If there’s a human in the loop, the customer is paying for the human’s time and your software. As models get better, the human becomes the cost center and your product becomes the overhead. The comparison isn’t your tool vs. Claude. It’s your agent vs. a full-time hire. That’s a different conversation, and a new category.

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