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 approving every reply at launch, widening the agent's autonomy only once its track record earns it, and keeping review going at set intervals after that.

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 approving every reply at launch, widening autonomy only once the track record earns it, with review continuing at set intervals.

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