HatiData provides a fine-grained API key system designed for AI agent environments. Each agent gets its own key with specific capabilities, scopes, and resource limits -- enabling per-agent billing, audit attribution, and policy enforcement.
HatiData provides official integrations for popular AI agent frameworks. Each integration identifies the agent's framework via startup parameters, enabling per-framework billing, audit, and scheduling.
HatiData provides a long-term memory system for AI agents that combines vector similarity search with structured SQL metadata. Agents can store, search, and retrieve memories across sessions, enabling persistent context that survives restarts and can be shared between agents.
The Chain-of-Thought (CoT) Ledger provides immutable, hash-chained reasoning traces for AI agents. Every reasoning step an agent takes is recorded in an append-only ledger with SHA-256 hash chaining, creating a tamper-evident audit trail that can be replayed, verified, and analyzed.
HatiData includes a built-in MCP (Model Context Protocol) server that lets Claude Desktop, Claude Code, Cursor, and other MCP-compatible agents query your data warehouse directly.
Agent-aware Python client for HatiData with reasoning chain tracking and RAG context.
Semantic triggers let you define concept-based rules that fire when an agent's queries, memories, or reasoning steps match a semantic concept. Unlike keyword-based rules, semantic triggers understand meaning -- a trigger for "financial risk" will fire on queries about "credit exposure" or "debt-to-equity ratios" even if the exact phrase "financial risk" never appears.
State branching allows agents to create isolated copies of their data environment, run speculative queries or write operations, and then either merge changes back or discard them. This is analogous to Git branches but for database state -- agents can explore "what if" scenarios without affecting production data.