Creatio agentic sales cookbook
Sales teams operate in an environment where expectations continue to rise. Sales reps must research accounts faster, personalize outreach at scale, prepare for increasingly complex customer conversations, and deliver accurate forecasts — often with fewer resources and higher workloads. Traditional automation tools can support isolated tasks but lack the ability to interpret context, intent, or the nuances of the sales process.
This article covers Creatio's agentic sales approach that addresses this gap.
Modern revenue operations are shifting toward a collaborative model that blends human expertise with AI-driven agents.
- Humans contribute judgment, strategic thinking, and relationship-building.
- AI Agents handle structured, repeatable, and time-consuming tasks such as summarizing information, preparing materials, and enriching data.
This partnership allows sellers to focus on meaningful interactions, while agents manage research, preparation, inspection, and documentation activities.
Purpose
This document provides a strategic, non-technical overview of how Creatio.ai supports agent-driven sales processes. It outlines:
- Key scenarios where the Agentic Sales approach adds value
- Stages of the sales cycle where each scenario has the greatest impact
- Roles and teams that benefit from deploying each agent
- Specific behaviors and tasks the agent performs
- Underlying capabilities that power the agent
This Cookbook is not a technical configuration guide. Instead, it explains how agentic scenarios function, how they complement CRM processes, and how organizations can adopt them to strengthen sales execution.
Learn more about Creatio.ai configuration and customization in separate guide: Creatio.ai.
This Cookbook is intended for sales operations personnel, revenue operations (RevOps) teams, business analysts, and system integrators involved in designing, configuring, or maintaining sales-related processes in Creatio.
Use this document to:
- Identify current sales challenges and match them to relevant agentic scenarios
- Understand how agents enhance existing CRM processes
- Prioritize where AI can deliver the highest impact
- Explore how Creatio's agentic capabilities evolve as sales needs grow
Agentic sales overview
In Creatio, an AI Agent serves as a role-specific virtual teammate equipped with the following:
- generative AI abilities
- predictive insights
- CRM and external data retrieval
- summarization and enrichment workflows
- automated follow-up actions
- guided next steps
Instead of manually searching for information, drafting content, or compiling notes, sellers can delegate these tasks to agents that execute them instantly and consistently.
The Creatio agentic sales approach is built around six agentic scenarios, each supporting a distinct phase of the sales cycle:
- Account research
- Quote generation
- Meeting preparation
- Territory management
- Forecast and pipeline review
- Field sales support
Together, they form a connected multi-agent Sales ecosystem that helps organizations reduce manual tasks, improve quality and speed of customer interactions, strengthen deal execution and sales velocity, and drive higher win rates at scale.
Composable AI architecture
Creatio's composable AI architecture makes it possible to:
- assemble agentic workflows using modular building blocks
- tune behavior
- connect additional AI Skills or third-party data sources
- trigger agents through CRM events or user actions
- extend Creatio functionality using no-code tools
This flexibility enables organizations to adopt agentic scenarios gradually, deploy them as productivity accelerators, or build toward a fully AI-supported revenue model.
AI Agents can be deployed as out-of-the-box accelerators or extended with new AI skills such as industry classification, predictive scoring, or intelligent summarization.
Key agentic sales scenarios
Scenario | Primary users | Core value |
|---|---|---|
SDR, AE, CS | Instant account minutes enriched with internal and external data | |
AE, Sales Ops | AI-assisted bundling, pricing, and proposal drafting | |
AE, CSM, Managers | Complete meeting minutes, agendas, and follow-up insights | |
Managers, RevOps | AI-supported forecasting and risk detection | |
Sales Ops, Managers | Data-driven territory balancing and whitespace identification | |
Field AEs, Partner Managers | Mobile-ready insights and automated post-visit actions |
Below are the six core agentic scenarios that collectively support the full revenue lifecycle.
Each scenario includes:
- Purpose
- agent behaviors
- AI Skills used
- considered configuration steps
- primary users
- manual effort eliminated
- manual workflow → Agent-assisted workflow comparisons
Account research agent
Purpose
Sales teams often spend excessive time gathering basic account intelligence across unconnected systems. This slows outreach, limits personalization, and increases the likelihood of outdated or incomplete information.
The Account research agent automates this work by enriching CRM records, identifying stakeholders, and generating ready-to-use account minutes.
Agent behaviors
- enriches account and contact records using internal and external data sources
- identifies decision-makers, influencers, and associated stakeholders
- summarizes recent activities and relevant updates
- produces unified account minutes that follow organizational standards
AI Skills used
- Enrich account data
- Enrich opportunity
- Identify contacts
- MatchlIndustriesCollection
- Search data
- Summarize activity
Configuration considerations
- account fit and segmentation AI Skills to highlight high-value accounts
- lookalike account to identify similar customers or patterns
- product interest or propensity AI Skills supported by external intent data
- auto-enrichment workflow triggered when a new account is created or assigned
- on-demand "Research this account" system action that aggregates insights into a single summary
Primary users
SDRs, BDRs, Account Executives, Customer Success Managers, Sales Analysts
Eliminated effort
Reps no longer need to:
- search across LinkedIn, company websites, spreadsheets, or internal tools
- ask teammates for account context
- assemble fragmented information from email threads or historical notes
Manual workflow → Agent-assisted workflow
Before (human):
- spends 30–90 minutes researching each account
- works with incomplete or outdated data
- skips research under time pressure
After (AI Agent):
- produces complete, structured account minutes in seconds
- keeps data enriched continuously
- enables instant personalization for outreach
Quote generation agent
Purpose
Manual quoting is often slow, inconsistent, and prone to mistakes. These delays reduce win probability and revenue predictability. The Quote generation agent accelerates the process by recommending bundles, suggesting pricing, and drafting proposal text.
Agent behaviors
- recommends product bundles and pricing options
- drafts proposal language and summary emails
- leverages historical deals to suggest effective configurations
- aligns quote options with opportunity context and account profile
AI Skills used
- Recommend products
- Generate product description
- Summarize lead
- Summarize opportunity
- Extend text
- Formal text rewrite
- Friendly text rewrite
- Prepare an email
Configuration considerations
- defined product bundles based on past successful deals
- "Deal size prediction" AI Skill to guide pricing ranges
- "Win/loss pattern" AI Skill to inform proposal structure
- "Generate Quote" workflow to create a draft with line items and messaging
- optional discount or approval workflows
Primary users
Account Executives, Sales Ops, Business Development, Revenue Operations teams
Eliminated effort
Reps no longer need to:
- track down pricing updates
- wait for product specialists to validate configurations
- copy content from outdated proposal files
- guess appropriate bundle combinations
Manual workflow → Agent-assisted workflow
Before (human):
- requests bundle options from territory managers
- awaits product team clarifications
- rewrites proposal content from scratch
- spends time assembling quotes manually
After (AI Agent):
- instantly recommends bundles, pricing, messaging
- drafts complete proposal content
- uses proven win patterns
- reduces approval cycles
Meeting preparation agent
Purpose
Sellers often piece together meeting context by searching across CRM notes, emails, calls, and documents. This process increases the risk of missed follow-ups and inconsistent preparation.
The Meeting preparation agent generates actionable meeting minutes, agendas, and post-meeting insights.
Agent behaviors
- prepares complete meeting minutes with history and relevant context
- suggests agendas, questions, and recommended actions
- summarizes recent interactions, stakeholder notes, and external signals
- processes post-meeting summaries and updates CRM records automatically
AI Skills used
- Prepare for the meeting
- Prepare meeting agenda
- Generate lead call script
- Summarize activity
- Summarize opportunity
- Enrich opportunity
Configuration considerations
- "Win likelihood" AI Skill to surface risk indicators
- "Close date prediction" and "Deal value prediction" AI Skills for prioritization
- next best action recommendations
- automatic "Prepare for Meeting" workflow triggered from calendar or opportunity page
- optional "QBR preparation workflow" for multi-opportunity analysis
Primary users
AEs, CSMs, BDRs, Sales Managers, Partner Managers
Eliminated effort
Reps no longer need to:
- search through emails, call logs, or historical notes
- rebuild agendas for each meeting
- conduct follow-up research after forgetting details
Manual workflow → Agent-assisted workflow
Before (human):
- scrambles to collect historical notes
- misses important details
- under- or over-prepares
After (AI Agent):
- generates complete meeting minutes in seconds
- summarizes risks, stakeholders, and updates
- proposes tailored talking points
Forecast and pipeline agent
Purpose
Leaders struggle to assemble accurate forecasts when data is inconsistent, outdated, or dispersed.
The Forecast and pipeline agent consolidates pipeline insights, highlights risks, and generates AI-supported forecast analysis.
Agent behaviors
- provides AI-supported forecast insights using pipeline inspection
- flags stalled deals, missing data, low-momentum opportunities
- summarizes risks, gaps, and progress indicators
- generates roll-up views for regions, segments, or teams
AI Skills used
- MatchlIndustriesCollection
- Summarize opportunity
- Enrich opportunity
- Search data
- Summarize activity
Configuration considerations
- "Win scoring" AI Skill to separate commit from upside
- "Close date prediction" and "Deal amount prediction" AI Skills
- risk-detection patterns trained on stalled or lost deals
- automated weekly forecast review workflow
- manager coaching workflows for high-risk opportunities
Primary users
Sales Managers, Regional Directors, RevOps, Sales Operations, CROs
Behavior
- summarizes pipeline segments
- highlights opportunities with limited activity
- provides projections informed by predictive AI Skills
Eliminated effort
Managers no longer need to:
- compile spreadsheet-based forecasts
- request constant updates from reps
- manually inspect each opportunity
- estimate commit numbers based on guesswork
Manual workflow → Agent-assisted workflow
Before (Manager):
- forecasts suffer from outdated or incomplete data
- reports require manual assembly
- early risks remain unnoticed
After (AI Agent):
- provides accurate, AI-driven forecast insights
- surfaces stalled deals and risk patterns
- delivers actionable coaching recommendations
Territory management agent
Purpose
Manual territory planning leads to imbalanced workloads, missed whitespace opportunities, and limited coverage.
The Territory management agent helps optimize territories using CRM data and performance indicators.
Agent behaviors
- maps accounts by industry, region, segment, or performance
- identifies whitespace and untapped opportunities
- suggests data-driven territory adjustments
- aggregates insights into a territory-level overview
AI Skills used
- Enrich account data
- MatchlIndustriesCollection
- Provide account conversion insights
- Search data
- Summarize activity
Configuration considerations
- define grouping criteria (industry, region, tier)
- configure thresholds for load imbalance
- set up periodic territory review workflows
- use CRM activity and opportunity data to identify concentration or whitespace
Primary users
Sales Managers, Territory Managers, Sales Ops, AEs
Eliminated effort
Sales Ops teams no longer need to:
- manually analyze spreadsheets
- reassign territories by hand
- consolidate dispersed regional data
Manual workflow → Agent-assisted workflow
Before:
- territories updated quarterly or annually
- decisions based on incomplete data
- reps overloaded while whitespace remains untouched
After:
- agent suggests evidence-based adjustments
- highlights whitespace and growth areas
- improves workload balance and coverage quality
Field sales agent
Purpose
Field sellers require quick, contextual insights while traveling or onsite. Working with the CRM manually is impractical in these conditions.
The Field sales agent provides mobile-ready insights, suggested talking points, and automated follow-ups.
Agent behaviors
- provides account and opportunity insights before each visit
- suggests priorities, actions, and talking points based on route and schedule
- logs notes and updates CRM through natural-language input
- drafts follow-up emails and tasks after each visit
AI Skills used
- Summarize opportunity
- Provide account conversion insights
- New meeting
- Prepare for the meeting
- Prepare an email
- Search data
Configuration considerations
- geo-prioritization AI Skills for route planning
- next-best-action AI Skills for post-visit actions
- automated "Visit Prep" workflow triggered by schedule or location
- post-visit wrap-up workflows for notes, tasks, and emails
- optional location-based opportunity heatmaps
Primary users
Field AEs, Partner Managers, CSMs, route-based sellers
Eliminated effort
Field reps no longer need to:
- print materials
- search CRM manually while traveling
- take handwritten notes
- rewrite emails after meetings
Manual workflow → Agent-assisted workflow
Before:
- juggles multiple tools while traveling
- logs updates at the end of the day or week
- misses context during onsite interactions
After:
- receives instant account insights on mobile
- records notes and updates automatically
- sends follow-up messages on the spot
Steps to adopt agentic sales
Creatio's agentic sales approach transforms how sales teams operate by bringing AI-driven intelligence, context, and automation into every stage of the revenue lifecycle.
Each agent — from research to field execution — helps teams reduce manual effort, automate repeatable workflows, ensure consistent preparation, and deliver more informed customer interactions.
The result is a connected, composable, and scalable multi-agent ecosystem that elevates both individual seller performance and organizational sales effectiveness.
Below is a recommended phased approach for deploying agentic scenarios in Creatio.
1. Identify the highest-impact pain points
Start by mapping organizational challenges to the six agentic scenarios.
Examples:
- Sellers spending excessive time on account research → Account research agent
- Inconsistent meeting preparation → Meeting peparation agent
- Forecasting friction and manual spreadsheet work → Forecast and pipeline agent
This helps prioritize high-ROI areas for initial deployment.
2. Identify 1–2 highest-priority agents
We recommend deploying agentic scenarios gradually.
Anchor candidates:
- Account research agent — boosts productivity for all sellers
- Meeting preparation agent — immediate impact on deal quality
- Forecast and pipeline agent — high value for managers and RevOps
These scenarios deliver fast wins and help teams adapt to agent-assisted processes.
3. Configure predictive AI Skills and workflows
Each agent uses a combination of:
- predictive scoring
- enrichment workflows
- summarization
- next-best-action logic
- historical pattern analysis
Tune these elements to align with your organization's customers, data sources, and sales workflows.
4. Integrate with existing CRM processes
Agentic behaviors should complement — not replace — your core processes.
Examples:
- Outputs provided by the Account research agent feed SDR outreach workflows.
- Minutes provided by the Meeting preparation agent integrate with calendar events and opportunities.
- Recommendations provided by the Forecast and pipeline agent connect to manager coaching workflows.
- Ensuring alignment with existing processes improves adoption.
5. Expand using additional skills and data sources
Creatio's composable architecture allows teams to extend agents with:
- third-party intent data
- industry classification AI Skills
- specialized summarization skills
- enrichment sources (LinkedIn, ZoomInfo, D&B, etc.)
- custom workflows tailored to your sales methodology
This enables iterative improvement without rewrites or complex development.