How to Build Midpoints: A Practical Guide to AI Automation With AI Agents

How to Build Midpoints

Midpoint is an AI automation platform built to turn intent into execution. You describe what you want to happen, Midpoint designs the workflow, connects your apps, adds AI agents where helpful, tests it end to end, and keeps it running.

This guide explains how to build Midpoints in a way that produces real outcomes, not demos. It includes the core concepts, a “first Midpoint” walkthrough, and the most reliable patterns for AI automation use cases across teams.

What a Midpoint is

A Midpoint is a production workflow that:

• Starts from a trigger (event, schedule, API call)
• Pulls context from your stack (Gmail, Slack, Sheets, CRMs, databases, finance tools, custom APIs)
• Uses generative AI and LLMs (Large Language Models) to extract, classify, summarize, or draft content when needed
• Routes outputs to the right systems, owners, and approvals
• Logs everything for visibility, retries, and monitoring

This is the practical implementation of agentic AI in business: systems that can interpret and propose actions, with guardrails like approvals and clear routing.

Step 0: Pick the right AI automation use case

The best “first Midpoint” has three properties:

1. High volume: it happens daily or multiple times per day
2. Messy inputs: emails, forms, requests, attachments, free text
3. Clear outcomes: create a record, update a system, send a summary, route an exception

Examples that consistently deliver ROI:

• Sales Inbox to CRM updates + next steps
• Support triage and translation to Linear/Asana + on-call alert
• Invoice intake to finance system + exceptions log
• Weekly pipeline and ops briefing to Google Docs + Slack summary

This is the same place U.K. enterprises are seeing value from automation and machine learning combined with LLMs: augmenting humans in high-volume, data-intensive workflows.

Step 1: Define the trigger

Midpoint can start from:

• Events: new email, new CRM record, new form submission, new payment, new ticket
• Schedules: daily digest, weekly briefing, nightly reconciliation
• APIs and webhooks: trigger from any app that can send a request

Rule of thumb: pick the trigger that fires closest to the moment work begins. That reduces latency, which is where automation creates the most value.

Step 2: Connect your tools and data

Midpoint is designed to work with your stack, not replace it. Start by listing:

• Source system: where the work arrives (Gmail, forms, support inbox)
• System of record: where the truth should live (HubSpot/Salesforce, finance tool, project tracker)
• Collaboration channel: where humans coordinate (Slack or Teams)
• Artifact output: where logs and summaries live (Google Sheets and Google Docs)

This is what separates AI automation tools that create outcomes from “chat-only” systems that create commentary.

Step 3: Decide where to use AI agents and LLMs

Use AI agents and LLMs (Large Language Models) when you need to:

• Classify intent (sales vs support vs billing)
• Extract structured fields from unstructured text (invoice totals, customer name, due date, line items)
• Summarize a thread into a clean handoff (context, decision, next step, owner)
• Draft a reply for human approval (fast, consistent output)

Do NOT use AI when:

• A deterministic rule is safer (routing by domain, matching IDs, required formats)
• The task requires empathy and nuanced exceptions
• The cost of a wrong action is high without review

The point of agentic AI is not autonomy at all costs. It is autonomy where safe, plus escalation where judgment matters.

Step 4: Add approvals and exception handling

To scale AI automation software sustainably, build two lanes:

• Happy path: the standard workflow runs automatically
• Exceptions lane: any uncertainty or mismatch gets routed to a human queue

Common exception triggers:

• Missing required fields
• Confidence below a threshold
• Amount mismatch or duplicates
• Unknown customer or account

This is how you keep automation reliable and trusted.

Step 5: Test it end to end

Before production, test with real samples:

• Old email threads
• Example invoices
• Real leads from form submissions
• A few CRM records across different segments

Your goal is not to “see it run once.” Your goal is to ensure it handles edge cases and produces stable outputs.

Step 6: Deploy and monitor

Once deployed, treat Midpoints like operational systems:

• Monitor execution logs
• Track exception rate
• Review approvals
• Iterate on prompts and routing rules

Midpoint is designed to ship fixes quickly. If something fails during execution, the workflow should tell you what broke and why, then you refine it and keep moving.

Build your first Midpoint: Sales Inbox to CRM (15–30 minutes)

This is one of the fastest ways to see real ROI and it uses nearly every core concept.

When an inbound sales email arrives, Midpoint:

• Classifies intent
• Summarizes context and proposed next step
• Creates or updates a CRM record
• Posts the summary in Slack for visibility
• Drafts a reply for approval

Trigger:

• New email in Gmail (or Outlook) to your sales inbox.

Inputs

• Email body + subject + sender

Thread history (last N messages)

• Existing CRM records (search by email/domain)

AI agent step (LLM)

• Intent classification: Sales, Support, Billing, Spam
• Extract: company, role, urgency, budget signals, product interest
• Output: summary + next step + suggested reply

Actions

• Create/update lead and opportunity in HubSpot/Salesforce
• Post to Slack: “New inbound, summary, owner, next step”
• Create a follow-up task with due date

Approvals

• Human approves the drafted reply before sending
• If CRM match is uncertain, route to an “exceptions” Slack channel