Production AI agents that plan, coordinate, and execute across your tools.
We engineer multi-agent systems with explicit supervisor-worker hierarchies, Model Context Protocol (MCP) tool bindings, and infrastructure-level human approval gates.
Single-prompt LLM wrappers fail when tasked with multi-step operational objectives. They hallucinate tools, drop context across days, and execute irreversible actions without human verification.
We build hierarchical supervisor-worker architectures. Specialist worker agents handle retrieval, data processing, and tool writes independently, governed by strict outer authorization gates.
A supervisor core breaks down complex, multi-step business objectives and routes sub-tasks to specialist worker nodes.
Agents connect to databases, CRMs, and internal APIs via Model Context Protocol and Agent-to-Agent standards.
Asynchronous task runners that persist state across days, wait for external webhooks, and resume without losing context.
Outer-boundary security barriers requiring operator cryptographic signatures before high-stakes writes or financial transactions occur.
OpenTelemetry-instrumented token traces, latency graphs, and deterministic replay debugging for every agent decision.