Workflows that read the case and act on it — not just pass it along.
Claude sits inside your existing workflow and does the parts a human would have to do — read the document, make the call, write the response — with the rest of the flow unchanged.
Cardinal Grid closed-won. Handoff fired:
Meeting notes
- • Slack: #cs-cardinal channel created · Marco assigned.
- • Notion: project workspace cloned from template.
- • Calendar: kickoff booked Tue Mar 12, 11:00 AM PT (45 min).
How does Anthropic (Claude) work for workflow automation?
Anthropic (Claude) works for workflow automation by powering an Arahi AI agent that runs the workflow end-to-end inside your existing tools — no code, no custom build. The agent connects to Anthropic (Claude) alongside the other apps your team already uses, watches for the triggers that matter for workflow, and takes the next step on its own while keeping a complete audit trail for review. Design complex automation workflows with a visual builder — no developers needed. Teams typically see eliminated across connected systems once the agent is in production. You stay in control: every action is logged, confidence thresholds are configurable, and anything ambiguous is queued for a human instead of being silently auto-completed.
Built in plain English.
You write the rule the way you'd describe it to a teammate. The agent reads the rule, breaks it into the actions it'll take, and confirms the apps it'll touch — before it does anything.
- 1Read the meeting transcript end-to-end
- 2Extract decisions, commitments, and next steps
- 3Update the deal record and advance the stage if criteria met
- 4Notify the right teammate with the relevant context
Get started in three steps
Connect Anthropic (Claude)
Authorize Anthropic (Claude) in your Arahi AI dashboard. The secure connection takes less than 60 seconds.
Configure Your AI Agent
Set up triggers, actions, and conditions specific to how your team uses Anthropic (Claude).
Deploy & Monitor Results
Your AI agent goes live immediately. Track tasks automated, time saved, and accuracy metrics in real-time.
Slack: #cs-cardinal channel created · Marco assigned.
Action items extracted; assignees notified in Slack.
Three deals moved to next stage; risks flagged for the AE.
Approve before it sends.
Every draft lands in a review queue. You approve, edit, or reject — the agent never acts on its own unless you explicitly turn that on for a workflow you trust.
Every action, with the reasoning attached.
Each step the agent takes is logged with what it did, why it did it, and which app it touched. Audit-ready, so security and compliance can sign off without backfilling.
- Agent2:47 PM
Updated Salesforce · Closed-won with the meeting outcome.
- Agent2:46 PM
Advanced deal stage; the criteria for Proposal were met.
Reason: Budget confirmed and decision-maker identified per stage definition.
- Agent2:45 PM
Wrote meeting notes for Handoff · Cardinal Grid · Closed-won $94K.
- Agent2:44 PM
Read the transcript and extracted action items.
- Agent2:30 PM
Triggered by call end event in Granola.
Frequently asked questions
Wherever a step requires reading prose and making a judgment call. Zapier, Make, n8n, Workato, Tray.io, custom flows — the agent slots in as a step, replacing the 'route to a human for review' branches that today create the bottleneck.
RPA repeats keystrokes. No-code agents typically use one model with a static prompt. The agent reads the case, considers the context, applies your judgment criteria, and acts — and surfaces what it's unsure about. The difference is judgment per case, not the same script repeated.
First-class. Each agent step has a confidence threshold; below it, the case routes to a human's queue with the agent's reasoning. The human's decision becomes calibration data for the agent.
Every agent step logs: the inputs, the decision, the reasoning, the action taken, and (if reviewed) the human's override. Exportable for SOC 2 / HIPAA / regulated industries.
Edit the agent's instructions in plain English. The agent re-reads its instructions on each run; there's no model retraining or redeployment step. New rules apply immediately and are versioned.
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