Stallion Agent Platform

The Problem

Most agent demos look impressive right up until you need to understand why they failed, why they got expensive, or why a tool call behaved differently than expected. I wanted a platform that treated agent systems less like magic and more like software: inspectable, extensible, and instrumented from the start.

What I Built

• Local-first agent runtime: Built a platform that could run locally for fast iteration without depending on a hosted control plane.
• Full-stack delivery: Implemented a React/TypeScript UI, a Hono API layer, and a Tauri desktop wrapper so the system could be used as an actual product rather than a collection of scripts.
• Plugin-driven architecture: Added a manifest-based plugin model to support domain-specific agent workflows without coupling them to the platform core.
• MCP orchestration: Integrated Model Context Protocol (MCP) servers with automatic lifecycle management so tools could be attached and swapped cleanly.
• Tracing and metrics: Instrumented the platform with OpenTelemetry plus Prometheus/Grafana so agent runs exposed latency, cost, tool usage, and failure patterns.

Why It Matters

This project became my sandbox for answering production-oriented AI questions:

• How do you make tool use debuggable?
• How do you know when an agent is expensive for the value it creates?
• How do you design plugins and schemas so domain workflows stay extensible?
• How do you trace multi-step behavior without relying on anecdotes?

Key Takeaways

• Agent systems need the same engineering discipline as distributed systems.
• Tool orchestration and schema design are product decisions, not just implementation details.
• Tracing, cost visibility, and evaluation loops are what separate a reusable platform from an entertaining demo.