How to Auto-Resolve Front Conversations with AI (2026)

What "auto-resolve" actually requires

Strip the buzzwords and a real auto-resolution is a short chain of decisions the AI has to get right in order:

1. Read the conversation. Not just the latest line — the whole thread, the customer's history, and any context sitting in the shared inbox.
2. Understand intent. "I was double-charged" and "why is my invoice higher this month" are the same underlying question in different words. Keyword matching misses that; a language model doesn't.
3. Ground the answer. Pull the true fact — the order status, the policy, the account state — from a knowledge base or a live system, so the reply is correct rather than plausible.
4. Write and send. Produce a reply in your team's voice and either draft it for review or send it directly on channels you trust.
5. Close the loop. Add the right tags, assign to the correct teammate or team, and mark the conversation handled — so reporting and routing stay clean.

Any tool can do step 4 in isolation. The value is in doing all five reliably, on a shared-inbox platform where support, sales, and ops all work side by side.

What native Front AI does well

Front has invested heavily here, and it deserves real credit. Its AI splits into three buckets — Analyze, Assist, and Automate — and each earns its place.

Autopilot is the autonomous piece. Per Front's Autopilot product page, it is "built to handle customer questions from start to finish without a human," working across email, chat, and SMS. With Autopilot Playbooks you define how a process runs — what data to collect, what actions to take across systems, and when to loop in a human — and Autopilot executes end to end, adapting to variation without you mapping every edge case. That is a legitimate auto-resolution engine, not a chatbot bolt-on.

AI Compose is the writing assistant most agents will touch daily: one click to make a reply sound more professional or friendly, fix typos, or change its length. Copilot goes further, drafting a suggested reply from your past conversations and help-center articles when an agent opens a message. Summarize condenses a long thread so whoever jumps in isn't reading a novel.

On the Analyze side, Smart QA uses AI to score agent responses across every conversation rather than a hand-picked sample, and Smart CSAT infers satisfaction without a survey. For a support leader, that all-conversation coverage is a genuine step up from manual QA.

If you want the full breakdown of each feature and where it lives, Front AI explained covers it in depth.

Where native Front AI stops short

Here's the honest part. Front's native AI is strong, but three things are worth naming plainly before you assume Autopilot alone covers "auto-resolve."

Assist is not resolve. AI Compose and Copilot make a human faster — they polish or suggest, but a person still reads and sends. That's deflection of effort, not deflection of the ticket. Only Autopilot actually closes a conversation without a human, and Front is clear that it's a separate, metered capability.

The billing is add-on and outcome-shaped. Per Front's pricing, Autopilot starts at $0.05 per conversation (Front also documents it billed per Resolution — a complete response delivered with no human intervention). Copilot is $20/seat/mo as an add-on or included on Enterprise; Smart QA is $20/seat/mo, Smart CSAT $10/seat/mo, with a Smart QA + Smart CSAT bundle at $25/seat/mo. AI Compose is included in base plans, capped around 200 daily actions per user. None of that is unreasonable — but it stacks on top of Front's seat pricing (Starter $25, Professional $65, Enterprise $105 per seat/mo), and a per-conversation or per-seat model can get unpredictable as volume grows.

AI Answers is gone. Front's older AI Answers chatbot is now legacy — the help center states flatly that it is "no longer available for purchase," and Front steers new buyers to Autopilot Resolve instead. If your team once evaluated Front on AI Answers, that product is effectively retired; the current story is Autopilot.

None of this makes Front's AI bad. It makes it a native layer with native constraints: it grounds primarily in Front's own knowledge base and past conversations, English is the officially supported language at time of writing, and deeper actions live behind Playbooks and connectors Front supports.

The auto-resolve flow with an AI agent layer

An AI agent layer sits on top of the Front you already run — it does not replace your inboxes, your rules, or Autopilot. It's the reasoning tier for the conversations you want fully handled, and it exposes the whole five-step chain as one agent you configure. Here's the flow, end to end:

1. The agent reads the conversation. When a message lands in a shared inbox, the agent ingests the full thread and any customer context, not just the last line.
2. It classifies intent. It decides what the person actually wants — a refund status, a shipping ETA, a policy clarification — regardless of the exact words used.
3. It grounds the reply. It answers from your connected knowledge sources, and for live facts it calls a custom tool that turns your REST API into something the agent can invoke — pulling a real order, subscription, or account record so the answer is true, not merely fluent.
4. It drafts or sends. On conversations you trust it with, it sends a grounded reply in your voice; on the rest, it drafts for a human to approve. You set the confidence bar.
5. It tags and assigns. It applies the right tags and routes the conversation to the correct teammate or team, so anything that does need a human lands in the right place already labelled. The mechanics of that routing are covered in smart routing, tagging, and teammate assignment.

The screenshot above shows a Macha Knowledge Base agent demonstrating exactly this capability inside the Macha dashboard — reading a conversation, producing a grounded step-by-step answer, and closing it out. (It's a Macha demo-org agent showing the flow; it isn't a live Front conversation.) To wire it into your own shared inbox, connecting Front to Macha to route conversations to AI and the Macha–Front integration walk through the live connector, and building an AI agent for Front covers the agent setup itself.

Per-conversation vs per-action: the cost model that matters

The pricing model shapes how confidently you can turn AI loose. Native Front AI meters on outcomes and seats; an agent layer like Macha meters on AI actions — each discrete thing the agent does — which decouples cost from your seat count and from a per-conversation counter.

Credits on the agent side are consumed per AI action, never per resolution — because automation and reasoning genuinely have different costs, and pricing them by the action is the honest way to reflect that. Outcomes vary; a well-grounded reply, a tag, and an assignment are concrete units of work you can meter. The current numbers live on the Macha pricing page, and Front's own tiers are broken down in Front pricing explained.

A short playbook to start

You don't have to auto-resolve everything on day one. The teams who get this right narrow the scope first:

1. Pick one high-volume, low-risk intent — "where's my order," "reset my password," "what's your return window." These are grounded, repeatable, and safe to answer automatically.
2. Connect the grounding. Point the agent at your help center and, for live facts, wire a custom tool to the one API that holds the truth (order status, account state).
3. Start in draft mode. Let the agent draft, have humans approve, and watch the quality for a week before you flip trusted intents to auto-send.
4. Let it tag and assign the rest. Even where it doesn't resolve, an agent that labels and routes cleanly makes the whole shared inbox faster.

That's the division of labour worth aiming for: Front and its rules stay the deterministic dispatcher, native Autopilot handles the workflows it's built for, and an agent layer takes the conversations that need real reading and grounded answers. The wider category context is in AI agents for customer service.

FAQ

Can Front auto-resolve conversations without any add-on? Not fully. AI Compose and Copilot help a human write and send faster, but only Autopilot actually resolves a conversation without a person — and Autopilot is a metered capability (starting around $0.05 per conversation) on top of your seat plan. The older AI Answers chatbot is legacy and no longer available for purchase.

What's the difference between deflection and resolution here? Deflection often means the customer got an answer without reaching a human, which assist tools can influence. Resolution means the conversation was actually closed correctly. Auto-resolve requires the AI to read intent, ground the reply in real data, send it, and close the loop — all five steps, not just a suggested draft.

Does an AI agent layer replace Front or Autopilot? No. Macha runs on top of the Front you already use through a live connector — it doesn't replace your shared inboxes, your rules, or Autopilot. You keep native Front doing what it's good at and add an agent for the reasoning-heavy conversations.

How is the AI billed if not per resolution? An agent layer like Macha charges per AI action — each discrete thing the agent does, such as generating a grounded reply or calling a tool — rather than per resolution or per seat. Automation and reasoning have different costs, so metering by action is the transparent way to price it. Current numbers are on the pricing page.

Can the AI pull live order or account data into a reply? Yes. Grounding a reply in real facts is what makes auto-resolution trustworthy. A custom tool turns your REST API into something the agent can call, so it fetches the actual order or account record before answering rather than guessing.

Ready to turn a routed, tagged Front conversation into one that's actually answered? Start a free trial of Macha and connect it to your Front in minutes.