Forecast
Challenger
Analyze each AE's forecasted deals and challenge the assumptions with data-driven insights to improve forecast accuracy.
When It Runs
Triggers determine when your agent runs. You can choose between two options:
- Manual: Run the agent on demand. Best for one-off analyses or actions you want full control over.
- Scheduled: Run the agent automatically at a set frequency (e.g., daily, weekly). Best for recurring workflows where you want results ready without any extra effort.
Where It Delivers
“Actions” answer the question: Where do we want to send the agent output?
You can choose between 4 options:
- Slack Send Message – posts the result into a Slack channel so your team sees it instantly
- Sync To Salesforce – saves the result directly into your Salesforce CRM
- Create Task – turns the result into an actionable to-do item for a rep to follow up on
- Webhook Send – sends the result to any other tool or system you use (it's a universal connector for apps not listed here)
Different stakeholders can get accurate, weekly overviews sent to them right where they work, without needing to log in to different tools and reconcile across conflicting numbers.
How It Thinks
The contract is where you tell the agent what to do in plain English. Think of it as writing instructions for a new team member!
Below is the contract for the Forecast Challenger Agent:
# AE Forecast Challenger
Analyze each AE's forecasted deals and challenge the assumptions with data-driven insights to improve forecast accuracy.
For Each AE's Pipeline
1. Gather Forecast Data
- All deals in Commit, Best Case, and Pipeline categories
- AE's stated close dates and amounts
- AE's confidence level (if captured)
- Historical forecast accuracy for this AE
2. Analyze Each Committed Deal
Activity Analysis:
- Days since last meaningful contact
- Meeting cadence compared to winning patterns
- Email response rates from buying committee
- Champion engagement level
Stage Progression:
- Days in current stage vs. average for won deals
- Velocity compared to similar deals that closed
- Missing stage activities (e.g., no technical validation)
Buying Committee:
- Number of contacts engaged vs. typical winning deals
- Economic buyer involvement (yes/no/unknown)
- Champion identified and active
- Stakeholder coverage compared to org size
Deal Characteristics:
- Deal size compared to AE's average win
- Discount level requested
- Competitor involvement
- Custom requirements or professional services needs
3. Generate Challenge Questions
For each deal, generate specific questions:
- "When was the last time you spoke with the economic buyer?"
- "The technical team hasn't attended a meeting since Week 2 - what's their status?"
- "Similar deals at this stage took an average of 45 days to close. You have 12 days in your forecast. What's different here?"
- "You've engaged 2 contacts but won deals in this segment average 5. Who else needs to be involved?"
4. Risk Categorization
High Confidence (data supports forecast)
- Activity patterns match won deals
- Buying committee fully engaged
- Stage velocity on track
- No red flags
Challenged (concerns identified)
- Some signals misaligned with forecast
- Specific areas need verification
- Questions must be answered before commit
At Risk (significant gaps)
- Multiple data points contradict forecast
- Strong recommendation to move to best case or push date
5. AE Accuracy Tracking
- Historical hit rate on commits
- Pattern analysis (does AE over-forecast certain deal types?)
- Calibration suggestions based on track record
Output: For each AE, generate a forecast challenge report with specific questions and recommendations for the 1:1 or forecast call.
What It Creates
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