Ledger Audit Agent
Real-time double-entry transaction auditing against PostgreSQL with zero-allocation APD arithmetic.
Built on Google ADK. Not glue code around a chat API. Compiled for high concurrency, explicit state boundaries, and deterministic tool execution.
Every agent call executes through an explicit state machine using Go channels and PostgreSQL unit-of-work transactions.
Ingests typed protocol buffer or JSON payloads directly into context-bound Go structs.
Generates execution steps via Google ADK Go SDK using Responses API schema bounds.
Executes native Go functions and raw SQL queries using DBTX transaction interfaces.
Validates response invariant checks and returns structured data to caller.
Sample Go agent deployments executing automated financial audits, AST refactoring, and analytical database queries.
Real-time double-entry transaction auditing against PostgreSQL with zero-allocation APD arithmetic.
Static analysis and automated refactoring agent for Go codebases with gopls integration.
Federated query engine combining transactional PostgreSQL state with DuckDB OLAP analytics.
Durable workflow monitor tracking container health metrics and auto-scaling events.
Most AI agent architectures wrap python scripts around generic chat HTTP calls, suffering from uncontrolled memory footprints, unhandled concurrency race conditions, and opaque tool execution loops.
Signal Machine builds on the official Google ADK Go SDK using the modern Responses API. Agent state transitions are compiled directly into Go binaries using goroutines and channel primitives. Database interactions use raw SQL through pgx/v5 and DBTX transactional boundaries.
We build custom, production-grade Go AI agents and durable telemetry workflows tailored for enterprise systems.