Customer-Facing AI Agent

How We'd Run It

The same six steps, applied to this project

1. 1

   Diagnose

   We pull real historical conversations and support tickets to find the patterns the agent needs to handle, not the patterns we assume exist.

2. 2

   Pilot

   We build a narrow pilot version of the agent for the most common conversation type, running in shadow mode or with a human approving every reply before it touches a full queue.

3. 3

   Review, Together

   We review real pilot conversations together — where the agent nailed it, where it faked confidence it shouldn't have — before deciding what it's ready to handle unsupervised.

4. 4

   Build

   We expand the agent to the full range of conversation types the pilot validated, wiring in the escalation path and the systems — CRM, order data, knowledge base — it needs to answer correctly.

5. 5

   Launch, With a Human on Every Send

   We launch to real customer traffic with a human still reviewing every reply for an initial period, widening the agent's autonomy only as its track record earns it.

6. 6

   Operate & Improve

   We keep monitoring conversation quality and escalation rates after launch, and retrain or restrict the agent the moment its accuracy on a topic starts slipping.

Typical Timeline

A rough phase-by-phase breakdown

A target shape for a project like this, not a narrated account of a specific past engagement — actual pacing depends on scope and how quickly decisions get made.

1. 1

   Diagnose & Data Audit

   Week 1

   Pull real historical conversations and support tickets to find the patterns the agent needs to handle.

2. 2

   Pilot in Shadow Mode

   Early Week 2

   Build a narrow pilot agent for the most common conversation type, running under full human review before touching live traffic.

3. 3

   Review Together

   Mid Week 2

   Walk through real pilot conversations together and agree what the agent has earned the right to handle unsupervised.

4. 4

   Full Build & Integration

   Weeks 2-3

   Expand to the full range of validated conversation types and wire in the CRM, order data, and escalation path it needs.

5. 5

   Launch

   Week 3

   Launch to real traffic with a human reviewing replies initially, widening autonomy only as the track record earns it.

6. 6

   Operate

   Ongoing from Week 3

   Keep monitoring conversation quality and escalation rates, retraining or restricting the agent as needed.

What's Included

What an engagement like this covers

• An audit of real historical conversations to find the patterns the agent needs to handle

• A narrow pilot agent tested in shadow mode or under full human review before touching live traffic

• Integration with the systems the agent needs to answer correctly — CRM, order data, knowledge base

• A tested escalation path so confused or upset customers reach a human, not a dead end

• A monitored launch with a human reviewing replies before autonomy is widened

• Ongoing conversation-quality monitoring and retraining as real usage reveals edge cases

Where Projects Like This Go Wrong

Common pitfalls, and how we handle them

• Launching an agent with full autonomy on day one instead of earning trust incrementally, so the first bad response happens in front of a real customer with no human in the loop

• Training the agent only on clean, happy-path conversations and never testing it against the angry, ambiguous, or off-topic messages that make up a large share of real traffic

• No clear escalation path to a human, so the agent either loops a frustrated customer or confidently answers questions it has no business answering

Targets, Not Results

What we consider success

These are the targets we'd work toward on a project like this — not results we're claiming to have already achieved.

• The agent resolves a defined share of real conversations correctly, with every customer-facing reply reviewed or approved by a human until it's earned more autonomy

• A tested, working escalation path so a confused or upset customer reaches a human within the same conversation, not a dead end

• A living log of every conversation the agent handles, so accuracy and edge cases are visible, not just assumed