Skip to main content
reopt Handbook
reopt Handbook
AI-Era GTM

Strategy Reset

AI GTM PrinciplesMarket and ICP ResetPositioning and Messaging

Demand Generation

AI Discovery and ContentSignal-led Demand GenAI Outbound

Revenue Motion

Hybrid PLG and SLGPOC and Value EngineeringSales Agent PlaybookPricing and Packaging

Retention and Expansion

Customer Success and ExpansionRevOps Data StackOrganization and GovernanceMetrics and Operating Rhythm

Execution

90-Day RoadmapTemplatesCases and Samples

Appendix

Verification ReportUpdates
Handbook›AI-Era GTM›Sales Agent Playbook
한국어English

Sales Agent Playbook

Attach sales agents to CRM and seller workflows with clear autonomy and quality controls.

Key takeaways

  • Sales agents should work inside the sales process, reducing research, prep, follow-up, coaching, and CRM friction while preserving seller judgment.
  • Map use cases from account research and meeting prep to next-best action, follow-up, coaching, and CRM hygiene, each with defined inputs and outputs.
  • Use five autonomy levels from L1 assistant to L5 autonomous; most teams should start at L1 to L3 and only reach L4 to L5 with clean permissions, logging, and rollback.
  • Agents must operate in CRM, show recommendations on the record with source links, and create tasks only after approval.
  • Guard quality against hallucination, over-automation, CRM pollution, recommendation fatigue, and playbook mismatch, feeding outcomes back into scoring.

Sales agents should work inside the sales process, not beside it. Their value comes from reducing research, preparation, follow-up, coaching, and CRM friction while preserving seller judgment.

Sales Agent Use Cases

Use caseInputOutput
Account researchCRM, website, news, hiring, usage logsAccount summary, trigger, hypothesis
Meeting prepPrior conversations, tickets, product usage, personaAgenda, questions, expected objections
Next-best actionStage, engagement, intent, riskRecommended action and reason
Follow-upCall transcript, commitments, product assetsEmail draft, task, mutual action plan
CoachingCall transcript, playbookImprovement points and role-play questions
CRM hygieneEmail, meeting, activity logField update draft and missing-field alert

Autonomy Levels

LevelDescriptionExample
L1 AssistantResponds when askedCall summary
L2 RecommenderSuggests next action"Send the security FAQ"
L3 Drafting AgentCreates execution draftsEmail, proposal, CRM update
L4 Human-approved AgentExecutes after approvalAdd to sequence, create task
L5 Autonomous AgentExecutes within policyLow-risk nurture, lead routing

Do not skip levels

Most teams should start at L1 to L3. L4 and L5 require clean permissions, logging, rollback, and approval policy.

Must Operate in CRM

RequirementWhy it matters
Show recommendation on account/opportunity recordSeller can judge in context
Provide source linksTrust and verification
Create tasks only after approvalRecommendation becomes action
Capture outcome feedbackRecommendation quality improves
Enforce permission-based accessProtect sensitive customer data

Quality Control

Failure modePrevention
HallucinationNo unsourced facts
Over-automationApproval required for high-risk actions
CRM pollutionShow field-update diff
Recommendation fatigueLimit number of recommendations per account
Playbook mismatchTie recommendations to official methodology

Operating Checklist

  • Sales agents run inside CRM or the seller's primary workflow.
  • Every recommendation includes source and reason.
  • CRM updates are drafts or diffs before approval.
  • High-risk customer actions require human approval.
  • Outcomes feed back into scoring and playbook tuning.

Related docs

CRM Standard

A handbook for designing CRM as an operating system across customer data, process, automation, and metrics.

RevOps Data Stack

Build CRM, data quality, and signal pipelines for AI-ready GTM operations.

Templates

Canvases, scorecards, calculators, and prompts for operating AI GTM.

CRM Strategy

CRM Standard · Align CRM purpose, ownership, and priorities from a leadership perspective.

Updates

CRM Standard · Change log and operating-standard updates for CRM Standard.

POC and Value Engineering

Prove AI product value quickly with customer data and a measurable business case.

Pricing and Packaging

Design AI-era SaaS pricing models, packaging, margins, and governance.

On this page

Sales Agent Use CasesAutonomy LevelsMust Operate in CRMQuality ControlOperating Checklist