AgentMesh Workflow Engine
Autonomous Multi-Agent AI Workflow Orchestrator
AgentMesh Workflow Engine
Startup
Bengaluru, Karnataka
Autonomous Multi-Agent AI Workflow Orchestrator
Delivered
3 Rounds
The Challenge
Enterprise operations teams were bogged down by manual data synthesis between fragmented CRMs, support tickets, and ERPs, while standard LLM chatbots lacked reliable tool-use execution.
How We Started
Combined graph-based state machines with strict schema validation, ensuring agents never execute unsafe database updates without explicit human sign-off.
Key Requirements
What We Delivered
Visual multi-agent workflow designer
LangGraph execution orchestration server
Human approval modal with Slack action buttons
Comprehensive telemetry and token cost audit log
Engineering Timeline
State Machine Graph
Engineered cyclical graph runner in Python with checkpoint persistence.
Visual Node Canvas
Built interactive drag-and-drop workflow canvas with custom connection ports.
Human Review Gate
Created real-time approval queues with web and Slack interactive buttons.
Enterprise Pilot
Automated vendor invoice reconciliation across 3 enterprise operations teams.
Client Sign-Off & Knowledge Transfer
Completed in 3 structured revision rounds. What was handed over:
- Dockerized deployment on enterprise cloud infrastructure
- Agent prompt engineering and tool definition manual
- Token budget cap and rate limit configuration guide
- 92% of repetitive cross-platform data reconciliation tasks completed autonomously.
- Over 1,800 engineering and operational man-hours saved every month.
- 64 enterprise API integrations supported out-of-the-box with zero hallucinated mutations.