everyone is giving their agents more memory. almost no one is giving them a feedback loop. an agent with the same context and no feedback repeats its mistakes forever. the model is the part you can't control. the "harness" around it is the part you can — and tuning it is how agents actually get better: blog.watolabs.com/ai-agents-…

Agents need scoped context. The real waste is searching the wrong systems, reading stale docs, and producing output humans have to fix. Our thoughts on how to make agents more efficient at work: blog.watolabs.com/ai-agents-…

We’re moving past the phase where “AI adoption” just means giving everyone access to more tools. The next phase is turning the best AI use cases people discover into repeatable workflows the whole company can trust. That means named workflows, shared context, connectors, permissions, review steps, and measurement. Wrote more here: blog.watolabs.com/becoming-a…

I think we’ve all started to realize that AI spend is not the same thing as productivity. The first wave of AI adoption was about giving teams access to better models, more tools, and more tokens. The next wave is going to be about efficiency: understanding which AI work is actually creating business value, and which work is just adding cost. How can businesses be best suited for this transition? Wrote more here: blog.watolabs.com/ai-outcome…

Most companies already have memory. It’s just scattered across Salesforce, Slack, Linear, GitHub, dashboards, docs, spreadsheets, and the people who know where to look. That’s why “company memory” for AI agents should be structured to help agents navigate this convoluted mess. Try to create a map: Write where knowledge lives, what sources to trust, who has access, and which workflows have been reviewed. Wrote more here: blog.watolabs.com/company-me…

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Collaboration is changing. The next teammate in your Slack channel might be an agent: one that understands team context, uses approved tools, works in a sandbox, and keeps going while you move on. Wato Cloud Agents are our first step toward that future.

Asked Wato one messy sales question: “What’s going on with BrightCart, why is the renewal at risk, where’s Eng on the fix, and what should I email them?” It pulled the account status, meeting context, technical blockers, stakeholder guidance, and drafted the follow-up. This is what happens when company context actually works.

We just built the most scalable company brain. Institutions are naturally noisy. Wato is built to solve for that. Most “AI memory” systems are solving the wrong problem. They treat memory as retrieval: put documents in a vector database, search across them, and hope the right context comes back. But company knowledge does not stay clean on its own. It gets stale, duplicated, contradictory, and hard to trust. Real memory needs structure: source of truth version history rollback permissions exact search human-readable records This is where a lot of graph RAG / embedding-first systems break down. They can retrieve plausible context, but they don’t answer the more important questions: Who said this? Is it still true? Which team owns it? What changed? At Wato, we think memory should work more like a versioned filesystem. Keep the source as readable Markdown, organized by teams and folders. Then layer search on top. You still get semantic search, keyword search, and exact search at scale, but the source stays inspectable and editable. The agent needs to both “remember" and the institution needs to know what it knows, keep it current, and make it safely usable.