Autonomous AI Sales Agent

Build an autonomous AI sales agent that researches prospects on LinkedIn, generates personalized outreach emails, schedules follow-ups, tracks engagement signals, and books meetings with qualified leads automatically.

Epics

Features

BDD Scenarios

Tasks

138

APIs

System Architecture

pattern

Event-driven agent architecture with tool orchestration

database

PostgreSQL for state persistence + Redis for agent task queues

agent Framework

LLM orchestration with ReAct pattern (Reason → Act → Observe loop)

tools

LinkedIn scraper, email sender, calendar API, CRM sync, web research

guardrails

Output validation, rate limiting, human approval gates, content safety filters

observability

Structured logging per agent step, decision trace, cost tracking per action

auth

API key authentication for agent triggers + OAuth for user dashboard

deployment

Docker containers with auto-scaling based on agent queue depth

Delivery Blueprint — 6 Epics

Core agent runtime that manages the ReAct loop, tool selection, memory, and execution flow. The brain of the autonomous sales agent.

ReAct Execution Loop

Implements the Reason-Act-Observe pattern where the agent reasons about the next step, selects and executes a tool, observes the result, and decides the next action.

Agent researches prospect and drafts personalized emailpositive

Given the agent receives a new prospect (name: Sarah Chen, company: Stripe)
When the agent executes the outreach workflow
Then step 1: Agent reasons 'I need to research this prospect before reaching out'
And step 2: Agent calls LinkedIn scraper tool → retrieves title, recent posts, company info
And step 3: Agent reasons 'Sarah recently posted about API reliability — I should reference this'
And step 4: Agent calls email drafting tool → generates personalized email mentioning API governance
And step 5: Agent calls email sending tool → sends the email
And the complete reasoning trace is logged for review

+1 more scenario

●Implement ReAct loop engine with configurable max steps (default: 10) and timeout (default: 120s)

●Build tool registry that maps tool names to execution functions with input/output schemas

+3 more tasks

Agent Memory & Context Management

Short-term (current conversation) and long-term (prospect history) memory that the agent uses to make contextual decisions.

1 scenarios, 4 tasks — unlock to view

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Governance Rules (6)

Every outgoing email must pass content safety filter before sending

Agent decision traces must be logged and retained for 90 days minimum

+4 more governance rules — sign up to view

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