AI teammates that do recurring work with your team

Chat tools answer when asked. Agents work on schedule.

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

Copilots assist. Agents do the recurring work.

A copilot suggests what to do next while you work. An agent does the recurring work with your team: reads data, reasons, takes actions, and delivers a finished result, with a human owner reviewing the moves that matter. You review the output, not the process.

Single-use vs persistent memory across runs

Most AI tools start fresh every time. Your agents remember what happened in previous runs and draw on shared company context. Each execution builds on the last, so the work gets sharper the more your team uses it, 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 an agent 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 agents

Once one workflow works, add adjacent tasks. Reuse templates and thresholds from your first agent. One ads monitor becomes ads plus SEO plus pipeline checks.

• 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.
• Your agents remember context across runs via persistent memory.

Free trial, then paid plans

Start with a free trial: enough to test one agent on real tasks. When you're ready, paid plans add more runs, more agents, and priority support.

How to measure ROI from AI agents

Track three things: recurring work that now ships consistently, time from issue detection to action, and how much higher-value work the team takes on once the agents own the recurring loops. The return is the work that finally gets done and the people freed to do what only they can.

High-ROI work, done consistently

A daily monitoring run delivers work a busy team rarely gets to: a check that runs every day, a report that always ships, an anomaly caught before it costs you. The math is simple: high-ROI recurring work done reliably, while your people spend their time where it moves the business.