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An agent that actually does the work

Ottic gives you an AI agent that does real work end to end — not a chatbot that just talks. You describe a goal in plain language, and the agent researches, runs code, browses the live web, queries your data, and hands you a finished deliverable. Most “AI tools” stop at suggestions. Ottic runs in a cloud sandbox with a real terminal and a real browser, so it can pull your analytics, scrape a competitor page, crunch a spreadsheet, hit an API, and produce a report or export you can actually ship. It is general-purpose. Today people lean on it for go-to-market work — analytics, SEO, reporting, competitive research — but the same agent can run a database query, reconcile a CSV, draft outreach, or automate an internal process. The job changes; the way you delegate does not.

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One agent, one workspace

The web app is your home base: chat, Skills, Routines, Files, and Artifacts (the reports and exports the agent produces). Everything lives in one place with the same history and the same connected tools. You delegate in chat. The agent streams its work as it goes — you see the tools it calls, the steps it takes, and the reasoning behind them — and the output lands as an artifact you can open, download, or share.
Coming soon: reaching your agent from more places — email-in and additional channels. Today, you work with it in the web app, and it can email results out and run on a schedule.
The way you ask changes what you get back. Compare:
Audit my site.
The agent has to guess which site, which pages, and what “audit” even means to you.
Run a technical audit of example.com — pull indexation and Core Web Vitals from Search Console, and give me the top 10 fixes ranked by impact.
Same task, but now it knows the target, the data source, and the output you want. The more context you give, the more confidently it can act — and that is true whether you are asking for an SEO audit, a sales pipeline summary, or a data reconciliation.

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The delegation mental model

Think of Ottic like a new hire who is smart, fast, and eager — but does not know your preferences yet. On day one you give small, well-defined tasks and check the work. Over time you hand over more, because it has proven it can handle the small things. The difference: Ottic starts with a head start. Connect it to your tools and it is not working from zero — it is a hire who did their homework before day one.

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It gets more useful over time

Ottic gets sharper the more you use it — but not because it silently learns you in the background. It gets better because of two things you control:
  • Connect more tools. Each connector gives the agent another source of truth to work from — richer context, better output.
  • Save what works as Skills. When a prompt or workflow nails it, save it as a reusable Skill. Your Skills library becomes your team’s institutional memory — the way you do reports, audits, and outreach, captured once and reused everywhere.
That is the loop: delegate, connect, save the wins. The agent that felt generic on day one starts producing work the way your team would do it — because you taught it explicitly, in Skills, not by hoping it guessed.

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What’s next

You understand what Ottic is and how to talk to it. Next, we cover how connecting your tools gives the agent a fuller picture of your work — and why that changes everything it can do.