MCP-Based AI Agent
Workflow Engine
An MCP (Model Context Protocol) server that gives AI agents structured, audited access to internal systems — exposing curated read endpoints as typed tools. Extended into an agentic layer (powered by Groq's Llama 3.3) that executes actions: scheduling support calls, booking appointments, and guiding users through multi-step service flows without human initiation.
Every internal operation required someone to manually trigger it — at scale, this becomes a full-time job.
Generating daily reports, querying customer data for specific conditions, triggering Slack alerts based on thresholds, updating CRM records after support resolutions — each of these required a human to log in, run the operation, and verify the result.
The internal team was spending 6+ hours per week on mechanical operations that followed deterministic logic. The goal: give an AI agent the ability to execute these workflows autonomously — with full audit coverage and zero trust violations.
MCP server as the structured interface between AI and internal systems.
MCP as the trust boundary between agent and system.
The system started by exposing selected internal GET endpoints via MCP — structured read access to operational data for AI queries. The MCP server provides a typed tool registry: every exposed endpoint has a schema-validated contract for input shape, output shape, and permissions required. The agent cannot call what isn't registered, and every call is logged before execution. This foundation of audited access is what made extending to write operations (scheduling, booking) safe enough to deploy to production.
Each MCP tool is a Lambda function. This means tools are isolated from each other, scale independently, and cannot reach outside their defined IAM permissions. A DB query tool cannot touch Slack; a file operation cannot hit the database. The blast radius of any misbehaving tool is bounded by its function boundary.
Every tool invocation writes an immutable record before execution: who triggered it (the AI session ID), what was called, what parameters, what timestamp. Post-execution appends the result or error. The audit table has no UPDATE or DELETE access for the application layer. If regulators or ops ever ask 'what did the agent do last Tuesday?', the answer is a SQL query.