Your processes, on autopilot.

A chatbot answers questions in a conversation. An automation runs a fixed sequence of steps whenever it is triggered. An AI agent sits between them: it has a goal, decides which tools to call and in what order, and adapts when reality differs from the plan. Most real business value comes from combining all three — an agent that reasons, tools that act, and a fixed workflow that guarantees the process runs.

The processes that pay off first are inbox triage and routing, quote and proposal drafting, lead enrichment and CRM data hygiene, invoice and document extraction, meeting follow-up, reporting, and first-line customer support. Anything that is high-volume, has a clear input and output, and currently eats hours of copy-paste work is a strong candidate.

Reliable enough for production when they are scoped narrowly and supervised properly. Impacter agents run against a defined tool whitelist, log every run and tool call for audit, retry on failure, and escalate to a human approval step for anything consequential. The failure mode we design for is an agent that stops and asks, never one that quietly does the wrong thing at scale.

Yes. Agents connect to 250+ tools including Gmail, Outlook, Slack, HubSpot, Salesforce, Notion and Google Drive through per-workspace OAuth, and can call your internal APIs directly for anything custom. Each agent only gets the specific tools you grant it, enforced server-side rather than by prompt instructions.

In practice they replace tasks, not people. The work that automates well is the repetitive, low-judgement portion of a role — triage, data entry, first drafts, status chasing — which is also the part employees least want to do. Every Impacter engagement includes training so the team moves up to the review, exception-handling and orchestration work that remains.

Through explicit approval steps. Any agent action you class as consequential — sending an external email, issuing a refund, changing a record — pauses as an approval that a named reviewer resolves from the dashboard, an email button, Slack or their IDE. Every decision is recorded in an audit log with who approved it and from where.

Every run is logged with its inputs, tool calls and output, so a mistake is traceable rather than mysterious. Consequential actions sit behind human approval, so most errors are caught before they reach a customer, and agents can be rolled back to a previous configuration. When something does slip through, the run log tells you exactly which step went wrong and why.