Intercom Launches Data and Action Suggestions to Help Teams Build Smarter AI Agents.

Intercom has rolled out Data and Action Suggestions for Fin, a feature designed to help support teams identify and close the gaps that prevent their AI agents from resolving more customer conversations. The update surfaces the most common queries that fall outside Fin's current capabilities, then provides sample API documentation to help teams implement the missing data sources or actions.

The announcement, shared by the company on LinkedIn, addresses a recurring problem for support leaders: knowing where to start when configuring an AI agent. According to Intercom, many teams shelve advanced setup work because the prospect of defining custom data schemas or building API integrations feels overwhelming, even when the potential automation gains are significant.

Fin already handles straightforward questions by pulling from help content, as previously covered when the company introduced simulation tools to test agent responses at scale. But conversations that require external data lookups or the ability to execute actions like processing refunds or updating account settings still require human handoff. Data and Action Suggestions aims to shrink that gap by making the configuration process more prescriptive.

The new capability analyzes historical customer conversations to identify patterns where Fin couldn't resolve an issue due to missing data or the inability to take action. It then ranks these gaps by frequency, showing support leaders which integrations or API endpoints would unlock the most automation value.

For each identified gap, Intercom provides sample API documentation tailored to the query type. This reduces the technical lift for teams that may lack dedicated engineering resources or API expertise. The company positions this as a way to move high-value automation work off the backlog and into production faster.

Christine, a member of Intercom's team, walked through the functionality in a video embedded in the LinkedIn post. While the video itself wasn't available for review, the description emphasizes that the tool highlights exact queries that could be automated, giving teams concrete examples rather than vague recommendations.

This update continues Intercom's effort to make Fin more autonomous and capable of handling complex workflows. The company has steadily added features that let teams build multi-step procedures and test agent behavior in controlled environments, as seen in recent updates to Fin Procedures and simulation testing.

By surfacing configuration gaps based on real usage data, Intercom is attempting to shift the burden of agent optimization away from manual guesswork. The approach mirrors how analytics tools in other categories, like PostHog for product teams or Make for workflow automation, surface high-impact opportunities based on behavioral patterns.

For support teams already using Zapier or n8n to connect Intercom with other tools, this feature could serve as a guide for which integrations to prioritize. The sample API docs also make it easier to hand off requirements to engineering or no-code builders working in platforms like Xano or Supabase.

The feature is most relevant for teams that have already deployed Fin and are looking to increase its resolution rate. Early-stage users still building out their help content may not see immediate value, since the tool depends on a baseline of conversation data to generate meaningful suggestions.

Intercom has not disclosed pricing details or whether Data and Action Suggestions is available across all Fin tiers. The company's focus on reducing setup friction suggests it's targeting mid-market and enterprise customers who have the volume and complexity to justify deeper integrations, but lack the bandwidth to scope them manually.

For now, the update represents another step in Intercom's broader bet that AI agents will handle an increasing share of customer support, provided teams have the right tools to configure them effectively. Whether that thesis holds will depend on how quickly support leaders can move from identifying gaps to actually implementing the integrations Fin suggests.