AutoFlow AI
Agentic AI workflow automating lead research, email drafting, and outreach campaign triggering for sales teams with autonomous decision-making and human-in-the-loop controls.
- Client
- GrowthForge
- Industry
- Marketing
- Type
- Agentic AI
- Year
- 2025
- Our role
- AI Engineer
- Timeline
- 8 weeks
- Team
- 2 developers
Overview
GrowthForge's sales team was spending 30+ hours/week on manual lead research and email drafting.
Process.
- Built modular AI agents using LangChain with specialized roles: Lead Researcher, Email Drafter, Decision Agent.
- Connected agents through n8n for workflow orchestration.
- Implemented guardrails and human approval loops.
What we built.
- featAutonomous lead research agent with web search capability
- featProspect company research and analysis
- featPersonalized email generation based on research
- featDecision agent for lead scoring and prioritization
- featCampaign trigger automation through email systems
- featHuman-in-the-loop approval for outreach
Hard parts.
fixInconsistent AI output and email quality
Built email templates with placeholders, implemented multi-stage prompts with refinement, added validation rules, and used feedback loop to improve outputs. Email usability improved to 78%.
fixAgent decision-making inconsistency
Created standardized decision frameworks with explicit rules, added decision logging for auditing, implemented confidence scoring, and created human review for borderline cases. Consistency improved to 94%.
fixLack of context and previous interactions
Integrated with CRM to pull interaction history, built memory system with vector database for prospect context, and added interaction checks before outreach. Duplicate outreach eliminated.
fixDifficulty controlling and monitoring autonomous agents
Implemented strict approval workflows for outreach, created admin dashboard for monitoring agent decisions, added audit trails, and enabled pause/restart controls. Full transparency achieved.
Built with
Python LangChain OpenAI n8n
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