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Clearskies gives you templates to get started quickly, plus the flexibility to build custom agents from scratch. Here’s what teams are building. Most of these are built from two pieces. An agent is the reusable AI unit that does the thinking, a model, your instructions, and optionally web search. A workflow is what makes it run on its own: the workflow owns the trigger that starts things off, and the steps that find records and take action, like posting to Slack, sending an email, or writing to your CRM. An agent answers when it’s asked; a workflow is what puts it on a schedule or runs it after a meeting.

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AskAI Assistant

The problem: Reps spend time hunting through CRM, calls, and emails to answer basic questions about accounts and deals. The solution: A Slack-based Q&A agent that answers questions about any account, deal, or contact using the full context graph. What this looks like: Ask “what happened on the Acme deal last week?” and get an answer in seconds, not minutes of digging through tabs.

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Deal Risk Monitor

The problem: At-risk deals slip through the cracks. By the time you notice the warning signs, it’s too late. The solution: A workflow that runs on a schedule, finds your open deals, runs an agent to assess engagement drops, champion changes, and other risk signals, and posts what it finds, before deals go dark. What this looks like: Surface at-risk deals automatically instead of hunting through CRM. Get alerts when engagement drops or key contacts go silent.

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Post-Call Workflow

The problem: After calls, reps forget to update CRM, send follow-ups, or capture next steps. Important context gets lost. The solution: A workflow that fires when a meeting ends, runs an agent to summarize the call, then writes the summary back to your CRM. What this looks like: Calls get summarized and logged without manual data entry. Follow-up tasks get created automatically.

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Pipeline Hygiene

The problem: CRM data quality degrades over time. Close dates slip, stages don’t match reality, and forecasts suffer. The solution: A workflow that runs on a schedule, finds the deals to check, runs an agent to identify data quality issues, and either writes the fix back to your CRM or flags it to the rep. What this looks like: Keep your pipeline clean without endless “update your deals” Slack messages. Auto-fix what can be fixed, flag what needs human review.

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Exec Briefing

The problem: Preparing for executive meetings means pulling data from multiple systems and manually assembling a brief. The solution: A workflow that fires ahead of the meeting, the meeting trigger takes a lead time, so it can run 15 minutes or a few hours before, and runs an agent that builds the brief from CRM, calls, and emails. What this looks like: A comprehensive account brief arrives before your QBR, automatically, in Slack, or in your inbox if you’ve connected Google.

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Deal Scoring

The problem: Qualification is inconsistent. Reps use different criteria, and it’s hard to compare deals objectively. The solution: An agent that scores deals against your criteria (MEDDPICC, BANT, or custom) using actual conversation and CRM data. What this looks like: Objective deal scores based on real signals, not gut feel. Identify gaps in qualification before they become pipeline problems.

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Call Coaching

The problem: Managers can’t review every call. Reps don’t get consistent feedback on their technique. The solution: An agent that analyzes calls against your coaching criteria and provides feedback. What this looks like: Scalable coaching feedback without requiring managers to listen to every call. Identify patterns across the team.

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The full use-case library

The examples above are the deep-dives, each one walks through the problem and the shape of the build. The list below is the full set of use cases we’ve documented, grouped by the job being done. Start here if you want to see everything; go back up if you want detail on one. They’re all built the same way. An agent does the reasoning. If it needs to run on its own, a workflow gives it the trigger and delivers the result. Before a call Walk in knowing the account, the people, and where the deal actually stands. After a call, and keeping the record current Capture what happened once, so nobody has to reconstruct it later. Deal and account inspection Check a deal against evidence instead of against what someone remembers. The leadership rhythm The recurring meetings, prepared before anyone walks into them. Patterns across the whole book of business What every conversation adds up to, read across accounts instead of one at a time.

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Start with a template or build from scratch

Every template is customizable. Modify the instructions, or combine multiple templates into something new. If your use case isn’t covered by a template, you can build custom agents from scratch using the MCP server or agent builder.

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Next steps

  • Quick start — Connect your data and deploy your first agent
  • MCP server — Build custom agents with full API access