Intercom Launches Fin Suggestions to Automate AI Agent Performance Tuning.

Intercom has released Fin Suggestions, a feature designed to help support teams improve their AI agent's performance by analyzing conversation data and recommending specific article edits.

The update addresses a persistent challenge for teams using AI-powered customer support: identifying where their knowledge base falls short and what changes will have the most impact. Rather than manually reviewing conversations to spot gaps, Fin Suggestions surfaces actionable recommendations based on patterns in how Fin, Intercom's AI agent, handles customer queries.

The feature monitors all customer conversations handled by Fin, then generates suggestions for improving article content. When Fin struggles with certain queries or provides incomplete answers, the system flags the underlying knowledge gap and proposes a fix.

What sets this apart from standard analytics is the execution layer. Fin Suggestions provides pre-populated, inline edits that users can review and accept with a single click. Instead of identifying that an article needs work and leaving teams to figure out how to fix it, the system drafts the changes itself.

Christine, featured in Intercom's demonstration, showed how teams can act on these suggestions quickly. The workflow eliminates the back-and-forth of determining what information is missing, how to phrase it, and where it should live in the article structure.

This builds on Intercom's broader push into AI-first support, where the company has been steadily adding tools that help teams manage and optimize automated customer interactions rather than just deploy them.

For support leaders, the value proposition is straightforward: less time spent on maintenance, more time on strategic improvements. Teams that previously allocated hours to reviewing AI performance and updating documentation can now compress that workflow into minutes.

The feature is particularly relevant as companies scale their AI agent usage. When Fin handles thousands of conversations, manually tracking where it underperforms becomes impractical. Automated analysis combined with ready-to-apply fixes makes continuous improvement feasible at higher volumes.

Intercom has been expanding Fin's capabilities across multiple dimensions this year. The company recently introduced real-time issue detection to alert teams as problems emerge, and launched Fin for Sales to bring the AI agent into a new department.

Fin Suggestions fits into that pattern of building operational tools around the core AI agent, rather than just adding conversational features. The focus is shifting from what Fin can say to how teams manage what Fin knows.

Intercom announced Fin Suggestions in a LinkedIn post on February 3, highlighting the speed at which teams can now act on performance insights. The feature is available to Intercom customers using Fin, though the company has not specified pricing tiers or rollout details.

For teams already using Fin, the update requires no changes to existing workflows. Suggestions appear within the Intercom interface alongside other content management tools, and users can review, edit, or dismiss recommendations as they come in.

The underlying analysis runs continuously, so suggestions reflect current conversation patterns rather than historical data. As customer questions evolve or new products launch, Fin Suggestions should surface relevant content gaps without manual configuration.