Autonomous AI agents that get work done

Chat tools answer when asked. Teammates work on schedule.

ChatGPT, Claude chat, and similar tools are reactive. You type a question, they answer. AI teammates are proactive: they run on a cron schedule, pull data from your apps, and deliver output without anyone opening a chat window.

Copilots assist. Agents own the task end to end.

A copilot suggests what to do next while you work. An autonomous AI agent does the work itself: reads data, reasons, takes actions, and delivers a finished result. You review the output, not the process.

Single-use vs persistent memory across runs

Most AI tools start fresh every time. AI teammates remember what happened in previous runs. Each execution builds on context from the last, so the agent gets more useful over time without re-explaining your setup.

Three permission modes: autonomous, approval, plan

Autonomous mode: the agent runs and acts without asking. Approval mode: you review every action before it executes. Plan mode: the agent proposes a plan, you approve, then it runs. Start with approval and promote to autonomous when trust is established.

OAuth with minimum permissions

Every app connection uses OAuth with the narrowest scope needed for the task. Read-only access for monitoring. Write access only when the workflow requires it. You control what the agent can touch.

Full execution logs for every run

Every action, tool call, and output is logged. You can see exactly what the agent did, what data it read, and what it produced. Complete audit trail for every run.

Escalation rules and error handling

When the agent encounters something it cannot handle, it escalates instead of guessing. You define what triggers escalation: missing data, confidence thresholds, or unexpected patterns. Errors are logged, not hidden.

Create a teammate in under 5 minutes

Give it a name, write instructions in plain language, and connect the apps it needs via OAuth. Start with one narrow task: a daily check, a weekly report, or an email triage workflow.

Define your first task and trigger

Describe what it should do: which data sources to read, what output format to produce, where to deliver results, and what to escalate. Set a cron schedule, webhook, or email forward as the trigger.

Review output and iterate

Start in approval mode so you review every action before it executes. Check the output after each run. Adjust instructions one variable at a time. Switch to autonomous when the output is consistently good.

Scale to more tasks and teammates

Once one workflow works, add adjacent tasks. Reuse templates and thresholds from your first teammate. Most teams go from one task to five within the first month.

• Narrow tasks produce more reliable output than broad ones.
• Every run has full execution logs and output files.
• Spend 20 minutes weekly tuning instructions. Small adjustments compound.
• Teammates remember context across runs via persistent memory.

Free tier and paid plans

Start free. The free tier gives you enough to test one teammate with real tasks. Paid plans add more runs, more teammates, and priority support. No credit card required to start.

How to measure ROI from AI agents

Track three things: hours saved on recurring work, time from issue detection to action, and reduction in manual report prep. Teams typically see ROI within two weeks of deploying their first workflow.

Cost per run vs cost of manual work

A daily monitoring run costs a fraction of what a team member spends doing the same check manually. The math is simple: if the task takes 30 minutes by hand and runs every day, automating it pays for itself in days.