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AgentStackPro: An OS for AI Agents
Langsmith + Temporal + OPA + Others — Unified for AI Agents
1 follower
Langsmith + Temporal + OPA + Others — Unified for AI Agents
1 follower
AgentStackPro is the all-in-one platform for teams building with autonomous AI agents. 🔍 Observe — Distributed tracing, session replay, cost analytics 🛡️ Govern — Constitutional policies, PII guardrails, human-in-the-loop approvals ⚡ Orchestrate — Durable workflows with DAG execution, crash recovery 🧪 Evaluate — Dataset-driven experiments, AI-powered scoring Drop-in TypeScript and Python SDKs. 2-minute setup. Free tier available. Built for the teams who need more than just LLM tracing
The Problem
As companies deploy dozens of AI agents, they face:
No visibility into what agents are doing, costing, or failing at
No governance — agents act autonomously with zero guardrails
No orchestration — no way to coordinate multi-agent workflows
Fragmented tooling — teams stitch together 5-6 tools for observability, evaluation, cost tracking, and compliance
The Solution
AgentStackPro is a single platform that replaces the entire AI agent operations stack:
🧩 Core Platform Capabilities (20+)
Category Features
Agent Registry Register, version, and manage agents with full lifecycle tracking
Durable Workflows Temporal-inspired DAG orchestration with sequential, parallel, and mixed execution patterns, crash recovery, and resume-from-checkpoint
Observability Distributed tracing, structured logging, session tracking, span-level latency & token attribution
Evaluations & Experiments Dataset-driven experiments, auto-evaluation with AI scoring (1-5 scale), A/B prompt comparison
Governance & Compliance Constitutional AI policies, formal verification with tamper-proof audit logs (hash-chained), governance reports
Guardrails Input/output content filtering, PII detection & redaction (15+ patterns: SSN, credit cards, AWS keys, JWTs, etc.)
Policy Gates Action-level allow/deny policies with cryptographic integrity (SHA-256 hashed), rate limiting per action
Human-in-the-Loop Approval workflows with expiration for high-risk agent actions
Decision Tables DMN-style rule engines with first/unique/collect hit policies
Cost Analytics Real-time token usage tracking, model-level cost attribution, budget alerts & thresholds
Inter-Agent Messaging Agent-to-agent communication with content audit (pass/fail/flag)
Versioned State Immutable state snapshots with diff comparison, export/import
LLM Cache Semantic caching with TTL, hit-rate analytics, cost savings tracking
Prompt Management Version-controlled prompts with AI-powered optimization suggestions
Call Pattern Detection Redundant/duplicate MCP Tools call detection with hash-based deduplication
Alerts & Notifications Configurable alert rules with Slack, email, webhook channels
Trace Annotations Human feedback tags and ratings on production traces
Session Replay Full session-level agent interaction history
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.