Slack
When your AI agent handles a customer issue that needs team input, it posts directly to your Slack channels, reads thread replies for context, and alerts the right people with reactions. Support becomes a seamless loop between your customers and your team's workspace.
Your AI agent reads, writes, and reacts in Slack channels, turning your team workspace into an extension of every customer conversation.
Slack
Discover how AI agents create a real-time bridge between customer-facing conversations and your team's Slack workspace.
A customer describes a complex technical issue your AI agent cannot resolve. The agent posts a structured message to your #engineering Slack channel including the error description, customer account ID, and steps already attempted. An engineer replies in the thread. The agent picks up the thread reply and relays the fix back to the customer. Zero context lost between support and engineering.
Multiple customers are asking about a service disruption. Your AI Agent reads the latest messages from your #incidents Slack channel, finds the most recent status update from the on-call engineer, and shares it with each customer who asks. One source of truth in Slack serves dozens of customer conversations simultaneously.
When a customer escalation is posted to Slack, your support lead needs to know it has been seen. Your AI Agent monitors for the eyes emoji reaction on escalation messages. If no reaction appears within 15 minutes, it can repost or mention the on-call person. Escalations never sit unnoticed in busy channels.
Slack
FAQs
The agent uses Slack's chat.postMessage API with the channel ID and message text. It can post to any public channel the bot has been invited to. Messages can include formatted text, links, and structured content. The agent identifies the right channel using the List Channels endpoint.
The agent can read messages from private channels only if the Slack bot has been explicitly invited to that channel. Public channels are accessible by default once the bot is added to the workspace. Channel history retrieval supports pagination for longer conversation histories.
Tars requests channels:read, channels:history, chat:write, users:read, reactions:read, and team:read scopes. These allow the agent to list channels, read message history, post messages, reply to threads, add reactions, and look up team members. You authorize these during the one-click OAuth flow.
No. The agent queries Slack's API in real time during conversations. Message history, user profiles, and channel data are fetched live and used only to generate the current response. Tars does not maintain a separate archive of your Slack communications.
Yes. The agent uses the thread_ts parameter to reply to a specific parent message. This keeps follow-up information organized within the existing thread rather than posting new top-level messages. It can also read all replies in a thread to gather context.
Slack's Workflow Builder automates actions within Slack itself. Tars connects Slack to external customer conversations. Your agent bridges the gap between customers on your website or WhatsApp and your team in Slack. It also integrates with 600+ other tools beyond Slack.
Yes. The agent can look up users by email or list workspace members, then include @mentions in messages using the Slack user ID format. This draws specific team members' attention to escalated issues or time-sensitive customer requests.
If the bot is removed from a channel, the agent will receive a not_in_channel error when trying to post or read messages there. It handles this gracefully by informing you that the channel is inaccessible and suggesting that the bot be re-invited to resume communication.
Don't limit your AI Agent to basic conversations. Watch how to configure and add powerful tools making your agent smarter and more functional.
Privacy & Security
At Tars, we take privacy and security very seriously. We are compliant with GDPR, ISO, SOC 2, and HIPAA.
Email - sales@hellotars.com
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