AI Database Querying

Let agents work through Adam.

Adam sits between users, agents, and internal systems. Eve checks access and records the action.

How Tool Calling Works

1

User sends chat request with database endpoint references

2

Adam connects to each endpoint's ATI server and discovers tools

3

Tool definitions sent to LLM alongside the conversation

4

LLM decides whether to call tools based on context

5

Adam verifies RBAC: does this user have access?

6

Tool result fed back into conversation (max 12 iterations)

Same permissions as the user. Always.

RBAC-Scoped AI Access

AI uses the same permissions as the user. No privilege escalation.

  • Read-only if user is read-only
  • Invisible endpoints stay invisible
  • Same identity system for AI and data

Natural Language Queries

Ask questions in plain English. Adam queries through Eve with your permissions.

  • No SQL required
  • Context-aware responses
  • Multi-database support

Tool Orchestration

Adam finds the right tools, calls them, and brings results back into the conversation.

  • Tenant-isolated tools
  • Cached tool discovery
  • Managed and user-registered tools

Tool Execution Metrics

Track attempts, failures, and latency for every tool call.

  • Attempt & failure counters
  • Latency histograms
  • Per-tool observability

Conversation Persistence

Keep long-running analysis or use ephemeral chats for one-off work.

  • Persistent and ephemeral conversation modes
  • SSE streaming with heartbeat keep-alive
  • Resume conversations across sessions

Token Accounting

Track token use by conversation, user, organization, and provider.

  • Per-conversation token tracking
  • Prompt and completion breakdowns
  • Organization-level usage aggregation

Build agents without opening a side door.

Adam keeps agent access governed through Eve.