One dashboard for everyone means the right information for none. Every enterprise operation is different. The workflows, the data, the people who need to act on it. A one-size-fits-all interface forces your team to work around it. Custom apps fit around how your team actually works. Custom apps allow you to build the exact interface each team needs - surface exceptions, track performance, or drill into the interactions that matter. Describe what you want and the AI builds it, or work directly in the code for deeper control. Custom apps sit directly on Context - the same data layer agents write to during execution. That means direct engagement with your agents: see what they're doing, surface what needs attention, and act on it. Not a reporting layer bolted on from the outside, but an interface that's built into the operation.

Hot take: If you're stringing together AI point solutions across your business and having them work in silos, you're solving the wrong problem. Each isolated workflow starts from zero. More automated, not smarter. Context breaks that - every execution teaches the whole system, not just one workflow. Full interview with @a16z in the thread ↓

Interfaces are how humans stay in the loop - on the work agents do and the data they collect. Your AI workforce runs continuously, handling interactions, making decisions, logging outcomes. Interfaces are how humans see what's happening, understand why, and act on it. Custom apps. Dashboards. Purpose-built for each team. Interfaces integrate directly with Context, pulling directly from enterprise data and the data agents produce, so every team gets a view that reflects how they actually work. An ops lead monitors live call outcomes alongside monthly trends. A support manager surfaces escalations that need attention. A finance team flags billing exceptions before they become disputes. The right information, for the right person, at the right time. Agents do the work. Interfaces make sure humans are always in control of it.

"Extreme ownership" is one of six values we live by at HappyRobot. A crucial one if you want to build something that matters. We asked the team what it means to them - in their own words. 👀

Thousands of agent interactions. A system that gets richer with every one. With Context, every part of your operation is mapped as a connected entity and agents write structured intelligence back to it with every interaction, so the next agent action always starts smarter than the last. When a customer calls about a boiler fault, support already knows the history, dispatch sends the right technician with the right part, and renewals flags the contract risk - agents across the enterprise reading and writing to the same system. With Context, your operation doesn’t just automate the task, it learns from it.

Context is what separates an agent that reacts from one that resolves. Every agent action generates data that lives in Context, which in turn improves the next agent action - and this intelligence compounds over time. Data extraction. Entity mapping. Memory. A standalone agent starts from zero every time. An agent that draws from Context knows the issue a caller raised yesterday - and picks up exactly where that conversation left off. And because Context lives at the platform level, every agent draws information from the same source of truth. A support agent captures a billing dispute. A sales agent sees it before the next outreach. Every agent gets smarter with the other’s work. Context is a living system that compounds with every interaction so your AI workforce never stops learning.

New job title coming to logistics: AI workforce manager. @DHLSupplyChain's team used to call this "soul-crushing work." HappyRobot agents do it now - so their team can focus on what actually matters. Read more more in @TransportTopics' 2026 Top 100👇 ttnews.com/articles/how-ai-t…

How do you test an AI agent in a language you don't speak? Adversarial agents - another AI trained to be the most difficult customer imaginable. Our FDE Álvaro Le Monnier explains how we made it work in Swedish 👇

Today, the world kicks off the World Cup. And so do we. We're excited to announce the opening of HappyRobot's Mexico City office! 🇲🇽 This is our seventh office, following San Francisco, Madrid, Barcelona, Chicago, Sydney, and New York City. Game on, CDMX! ⚽

Your agent should meet its adversary before it meets your customers or partners. Introducing Adversarial Agents: AI-powered mock users that try to break your agent by simulating hypothetical hostile scenarios to cover all possible 'unhappy' paths. Prompt injection. Topic derailing. Instruction overrides. Every angle that a bad actor or a frustrated caller might try or edge cases that may come up - test any and all possible scenarios before the agent goes live. Plus, get a full audit report measured against expected behavior showing exactly what held and what broke, with correction suggestions for each failure, so you can deploy with confidence. Red-team your AI agents before you ship them.

An agent that works ≠ an agent you can trust. HappyRobot built 3 layers to close that gap: Northstars, pre-deployment tests, and production audits. All in the same platform as your agents, so every correction instantly feeds back into the system and makes it smarter for next time.