Jesse Cravens

Enterprise AI Budgets Follow Proven Results 💼 Startups that demonstrate ROI through real deployments are the ones securing both enterprise contracts and early funding. The market now rewards business outcomes — not technical complexity. Companies like Engagesoft scaled to 120+ enterprise customers by focusing on measurable organizational impact, not just product innovation. 🏢 Enterprise Validation → Large-scale rollouts proving scalability and reliability 📊 Performance Metrics → Clear productivity and cost improvement percentages ⚡ Implementation Speed → Faster deployments reducing time-to-value 💰 Financial Impact → Direct correlation between AI adoption and bottom-line growth 🎯 Use Case Proof → Success in mission-critical workflows 🛡️ Risk Reduction → Measurable gains in compliance, security, and operational stability 📈 Growth Enablement → AI directly fueling expansion and efficiency 🔄 Operational Excellence → Continuous optimization and quality improvements Enterprise procurement has entered the evidence era. Budgets follow business cases — not buzzwords. Startups that quantify their impact, instead of promising it, are earning enterprise trust and investor confidence.

3

Decision Spaces: Putting Agents on Rails 2026 is the year of trusted AI, not demos, not pilots, but production agents making real decisions in regulated environments. 🚀 But most agent architectures aren't built for trust, they're built for impressive demos that make compliance teams nervous and auditors reach for their red pens. The problem isn't AI capability, it's where the control lives. Most agentic systems use probabilistic LLMs, that reason however they reason. You bolt on guardrails (massive context prompts, loads of post inference controls) hoping they catch the bad outputs, but when they don't, you can't explain why the agent decided what it decided. Black box in, black box out. 🎲 Decision Spaces change this entirely. Think of them as as rails where the agent learns through execution, captures context, and builds institutional memory. All within domain-grounded constraints that encode what's actually permitted including goals, policy, scope, risk, evidence, and provenance all built into the boundary itself. The agent has autonomy to navigate and adapt but it cannot hallucinate outside the bounds because the bounds are the execution environment. 🛤️ Context graphs solve a piece of the puzzle i.e. being the record of the decision but these records feed into the Decision Spaces, as the actual control planes Both capture context but only one controls what agents can actually do with it. What this means in practice: A deterministic language model encodes regulations, policies, and domain knowledge directly from source with 70-80% captured automatically and SMEs tuning the 20% that matters This means no months of upfront ontology work before you can start. 💡 Data Object Graphs provide the bounded Decision Space where every decision is captured with full context and provenance. I.e. giving you the same answer to the same question every time with a complete audit trail back to source documentation. When a regulator asks why your AI made a decision, you show exactly which constraints applied, which policy drove the outcome, and which evidence was considered, transparent, traceable, and trustworthy. ✅ The Decision Space learns and evolves because every execution captured with full context becomes precedent without becoming constraint. Our AgentDOG framework implements Decision Spaces. Agents discover new patterns, adapts to regulatory changes, and gets smarter through action giving you context that compounds but within rails that hold. 🔄 Controlled, bounded, transparent, but learning is how you build agents that regulated industries can actually deploy in production. If you're want AI and agentic systems but need to use them where decisions have consequences and trust isn't optional, then Decision Space driven Agents are for you, let's talk. Happy Friday🎯 Nebulyx AI

17

39 Comments

Stop Pitching Ideas, Start Prototyping Them – A How-To Guide Getting a product or feature idea prioritized used to mean pitching verbally, throwing together a slide deck, or sketching on a napkin. And even then, you needed the attention of whoever controls the roadmap. If you're in customer service, marketing, or any non-product role, you faced an uphill battle. Modern AI tools have changed that. We recently hosted a hackathon at Photobucket. Historically, hackathons relied on engineers to bring ideas to life, but we opened this one to the entire company – customer service, marketing, UX, everyone. I watched people who had never touched AI tools take their ideas and generate working prototypes in hours. The barrier to "show, don't tell" is gone. Why Prototypes Beat Pitches 1. Ambiguity dies on contact with reality. When your idea lives only in your head, it's perfect. The moment you try to build it, you discover the gaps. This isn't failure. This is sharpening. 2. Words are slippery. When you say "intuitive dashboard," I picture something different than you do. Too often, we leave the meeting nodding, thinking we're aligned. We're not. If a picture is worth a thousand words, a working prototype is worth a thousand meetings. 3. Stakeholders trust what they can touch. When you pitch verbally, people evaluate you. When you demo a prototype, people evaluate the idea. The conversation shifts from abstract to concrete. 4. You separate the doers from the talkers. The person who shows up with a working prototype has demonstrated they can move from concept to reality. How to Prototype Effectively 1. Start today. These tools have a low barrier to entry – start with a simple prompt, then iterate. The more context you provide, the better results you'll get. 2. Time-box ruthlessly. Give yourself 2-4 hours. Half of your ideas will die in the prototyping phase – and that's a gift. Better to discover a concept falls apart in an afternoon than six months into development. 3. Focus on function, not polish. You're building the minimum viable demonstration. Prove the idea works. The prototype that exists today beats the beautiful mockup you'll finish "next week." Everyone Has a Voice Now The best ideas don't always come from the people who can build them. They come from the customer service rep hearing the same complaint daily, the marketing lead noticing user patterns, the UX designer seeing friction nobody else does. Next time you have an idea worth sharing, don't schedule a meeting. Spend a few hours building something rough. Then walk in and say: "Let me show you what I'm thinking."

11

1 Comment

The API Economy Is Now Bigger Than SaaS Most people don't see it. But there's a massive shift happening in tech. The numbers: API economy: $2.5 trillion in 2024 SaaS economy: $195 billion APIs growing 35% YoY vs SaaS at 18% What's an API economy? Instead of selling software, companies sell access to their infrastructure. Examples: Stripe: $70B valuation on payment APIs. Twilio: $4.5B revenue from communication APIs. Plaid: $13.4B valuation for banking APIs They don't have a UI. No dashboards. Just pure infrastructure that other companies plug into. SaaS to API Convergence Shopify: 20%+ revenue from APIs/apps ecosystem. Salesforce: $6B from platform/APIs (growing fastest). HubSpot: API usage up 40% YoY, powering custom workflows.​ The future isn't standalone products. It's composable infrastructure. Companies don't want another tool. They want capabilities they can embed into their existing workflows. P.S. Is your product a destination or an ingredient? #APIs #TechTrends #SaaS #IndustryInsights

16

1 Comment

Sent this "stop wasting tokens" message to my team today. Point #3 made me chuckle, so I figured I’d share. My rules for AI code assist. 1. Create a product requirements document. You can even use the LLM to help you make the summary 2. Use TDD. Works great with an AI coder. Make sure they are testing the right functionality before they try to build it. 3. Write down your implementation guidelines, in detail.  How you want to implement it. Think of yourself as an architect working on a ticket and you're giving instructions to a total noob junior wannabe tech bro who takes everything literally and doesn't like to admit when they don't know something. 4. Direct the LLM to context that is relevant. and don't make it too large.  If you're in a monorepo or monolith codebase, that's a lot of tokens. Keep each piece of work small and manageable 5. Have it generate a development plan - this is one of the options in Antigravity and Zed - my tools of choice - probably also in Cline.  Tell it to think step-by-step and explain decisions. 6. Read it. Approve it. Step through the work. Don't thank it or say please. It's just wasted tokens. 7. if it starts getting out of hand. **stop**.  Revert. You cant use that slop anyway.  Better yet.  if it's wrong after 3 tries, stop, code it yourself, or reset the context and start over. it's not going to end well. disclaimer: this post was not generated by AI and intentionally contains mistakes and no f***king emojis

125

42 Comments

💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

20

In my experience, some of the most valuable insights emerge from leading operational teams through complex transitions, such as offshoring and centralization. What stands out is how these situations closely mirror the challenges in product managing AI agents. Both require the ability to deconstruct intricate processes, remove ambiguity, and establish clear frameworks for action. Just as operational excellence depends on breaking down and clarifying work for cross-functional teams, effective product management of AI agents hinges on precisely defining tasks, expectations, and decision boundaries to ensure optimal performance.

5