AgentStackPro: An Operating System for AI Agents

Hey Product Hunt! 👋

We built AgentStackPro because we kept hitting the same wall: AI agents are powerful, but once you deploy them, you're flying blind.

The problem: Teams are stitching together 5-6 different tools just to monitor, govern, and optimize their agents. One for tracing, another for guardrails, another for cost tracking and many others different tools for different things and it's a mess.

Our solution: A single platform that gives you:

🔍 Full observability — traces, spans, latency & token attribution down to the span level 🛡️ Built-in governance — constitutional AI policies, hash-chained audit logs, PII detection ⚡ Durable workflows — Temporal-inspired DAGs with automatic retry & checkpointing 💰 Cost intelligence — real-time token usage alerts & response caching to cut LLM spend 🧪 Evaluation suite — datasets, experiments, and auto-scoring to catch regressions before production and structured, deterministic rulebook that explicitly defines the business logic, policies, and constraints the agent must follow and memory & skills systems for AI agents, enabling Large Language Models (LLMs) to store, retrieve, and operate on data across multiple sessions. This is a core concept for developing advanced, stateful Agentic AI systems. Track each selection—like Model Selection, Sub-Agent Selection, and MCP Tool Selection—with reasoning explaining why it was selected.

We ship with zero-config SDKs for TypeScript and Python — 3 lines of code to connect any agent, any framework.

Built by a team that's spent years in ML infrastructure and observability platforms. We believe the next wave of AI isn't just about capability — it's about trust. And trust requires transparency, control, and accountability.

We're offering there AI Agents for free without any subscription. Would love your feedback — what features matter most to you?

🚀 Try it: agentstackpro.dev