Personalized Multi-Touch Sales Outreach Agent
Multi-agent systems that prospect, personalize, listen, and forecast. These are the hardest problems in the booklet — they demand reasoning under uncertainty, real business-tool integration, and agents that must earn trust before a human hands them outreach or revenue decisions.
Background
Generic cold emails suffer from low open and conversion rates; hyper-personalized multi-touch campaigns perform best.
Objective
Build an agent that researches each lead, drafts a personalized multi-touch outreach sequence (e.g., email + LinkedIn message across 3–4 touches), and adapts the sequence based on how the lead responds — continuing, escalating to a human, or stopping.
Key Features
1. Per-lead research agent that pulls recent, relevant public context (news, role, company activity) 2. Personalized message-drafting agent that references specific research points (not generic templates) 3. Multi-touch sequence planner with defined cadence and escalating intent per touch 4. Reply-classification agent (interested / not interested / objection / out-of-office / no reply) 5. Adaptive next-step agent that decides: continue sequence, pause, escalate to a human rep, or stop
Expected Solution
A stateful, per-lead pipeline: research → draft → send (simulated) → classify reply → decide next step Persistent state per lead so the agent remembers where each conversation stands A clear, visible decision log showing why the agent chose each next action
Suggested Technology Stack
Orchestration: LangGraph (stateful per-lead workflow) Research: Web search tool + LLM summarization Messaging (sandboxed): SendGrid/Gmail API in test mode, or a simulated inbox — never real unsolicited sends State/storage: PostgreSQL or SQLite for per-lead sequence state Classification: LLM-based reply-intent classifier
Expected Outcomes
Higher-quality, better-personalized outreach at a volume no individual rep could sustain manually Faster, more consistent lead follow-up with nothing falling through the cracks Clear human-escalation points so the agent augments, rather than replaces, the sales rep's judgment
Possible Use Cases
An SDR team automating first-touch outreach while staying personalized A small agency running outbound campaigns for multiple clients at once A founder doing early customer-development outreach
Evaluation Parameters
Personalization quality — does each message actually use the researched context, not a generic template? Soundness of the multi-touch sequence logic and timing Accuracy of reply classification Appropriateness of escalation-to-human decisions