agent-ai

Build Copilot agents for lead qualification by configuring typed roles like qualifier or SDR with specific personalities, capabilities, and system prompts containing business context and safety guardrails.

A RAG pipeline with pgvector handles document chunking by processing PDFs and texts into embeddings, enabling semantic search to retrieve relevant context during AI-driven conversation threads.

Prompt engineering and guardrails constrain outbound triggers by applying few-shot examples, tool usage rules, and safety constraints to action executors, ensuring reliable multi-channel outreach orchestration.

Manage conversation history for sales workflows by utilizing threaded conversations with action execution and semantic search, allowing Copilot agents to track context from initial contact to meeting scheduling.

The architecture supports integration hooks with Conductor to ensure end-to-end workflow orchestration, using edge functions for messages and action executors with audits for reliability and safety.

Configure custom Copilot agent roles when predefined types like prospector or scheduler lack the specific capabilities and personality needed for complex, multi-step AI-driven lead engagement and conversion workflows.