Pipeline reviews are one of the most important and most dreaded rituals in B2B sales. Managers spend hours clicking through deals, asking the same questions: Is the close date realistic? Who is the economic buyer? When was the last activity? An AI deal inspection agent handles this grunt work automatically, flagging issues in real time so pipeline reviews can focus on strategy instead of data hygiene.

What a Deal Inspection Agent Checks

A well-configured inspection agent evaluates every open opportunity against a set of quality rules. Here are the checks that deliver the most value:

Data completeness checks: - Required fields populated for the current stage (e.g., MEDDIC fields, decision criteria, budget confirmation) - Contact roles assigned (champion, economic buyer, technical evaluator) - Next steps documented and dated

Temporal checks: - Close date is in the past (a shockingly common issue) - Deal has been in the current stage longer than the historical median for that stage - No activity (emails, calls, meetings) in the last 14 days - Close date pushed more than twice

Pattern-based checks (where AI shines): - Deal value is significantly above or below the average for the segment - Stage progression skipped a step (e.g., jumped from Discovery to Proposal) - Sentiment in logged call notes has shifted negative - Stakeholder count is below the threshold for deals of this size

Architecture

The agent follows a straightforward pattern:

Component Technology Purpose
Trigger Cron job (daily) + webhook (on deal update) Runs proactively and reactively
Data extraction CRM API (Salesforce, HubSpot) Pulls opportunity, contact role, and activity data
Rule engine Python logic + LLM Deterministic checks for data and dates; LLM for sentiment and pattern analysis
Output Slack DM to rep, summary to manager Private flag to rep, aggregated view for leadership
Logging Database table Tracks every flag, resolution status, and time-to-fix

Building the Rule Set

Start with five rules. Seriously - just five. Pick the ones your managers already ask about in every pipeline review:

  1. Missing close date or close date in the past
  2. No activity in 14+ days on deals in active stages
  3. No contact with “Economic Buyer” role assigned
  4. Deal in same stage for more than 2x the median duration
  5. Required fields for current stage are blank

Once these are running cleanly, add more sophisticated checks. The LLM-powered checks (sentiment analysis, pattern detection) should come in phase two, after you have proven the basic system works.

Implementation tip: Assign a severity level to each flag - Critical, Warning, or Info. Only alert reps on Critical and Warning. Log Info flags for manager dashboards.

The Notification Strategy

How you deliver flags matters as much as what you flag. A poorly designed notification system creates noise and kills adoption.

  • Reps receive: A single daily Slack DM summarizing their flagged deals, grouped by severity. No email storms, no in-CRM pop-ups.
  • Managers receive: A weekly summary dashboard showing flag counts by rep, most common issues, and resolution rates.
  • Before forecast calls: An on-demand report that highlights every deal in the forecast with unresolved flags.

Measuring Impact

Track these metrics to prove the agent’s value:

  • Flag resolution rate - what percentage of flags get addressed within 48 hours
  • Pipeline accuracy - compare forecasted vs. actual close rates before and after deployment
  • Time saved in pipeline reviews - survey managers on time spent before and after
  • Data completeness score - percentage of deals passing all required-field checks

Key Takeaways

  • A deal inspection agent automates the repetitive checks managers do manually in every pipeline review
  • Start with five simple, high-impact rules before adding LLM-powered pattern detection
  • Deliver flags privately to reps first - positioning as coaching, not surveillance, drives adoption
  • Measure flag resolution rate and pipeline accuracy to prove ROI to leadership