Concept
The Information Extraction agent serves as the semantic digger of Software 3.0. It mines meaning from messy input — PDFs, calls, articles, chats — and converts it into machine-usable formats.
The core idea is simple but powerful: make everything structured. What used to be opaque becomes a table. What used to be a paragraph becomes a schema. This agent turns ambient knowledge into structured knowledge.
Extraction is not just parsing — it’s interpreting, relating, and grounding entities in context. This is foundational to building agentic systems that reason over real-world input.
Functional Logic
The agent follows a logic pipeline like this:
- Segmentation — Divides content into logical units (paragraphs, sections, sentences)
- Entity Recognition — Identifies key entities (people, dates, terms, values, topics)
- Relation Mapping — Finds links between entities (e.g. "X acquired Y on date Z")
- Canonicalization — Normalizes into structured formats (CSV, JSON, database records)
- Error Handling + Ambiguity Resolution — Uses follow-up questions or clarification prompts when uncertain
This logic can operate on:
- Contracts → Parties, terms, dates, obligations
- Meeting transcripts → Speakers, actions, decisions
- Research papers → Claims, evidence, sources
- Emails → Topics, urgency, recipients
Software Enabled
- Document Data Pipelines — Feed scanned docs into workflows, no manual tagging
- Compliance Monitors — Scan contracts and flag risk-triggering clauses
- CRM Populators — Auto-fill contact, deal, or client fields from interactions
- Semantic Index Builders — Extract data for search and retrieval systems
- Business Intelligence Transformers — Make raw, ambient data usable across dashboards and agents
This agent is how you build memory — not by storing what was said, but by extracting what matters.
