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polos
by polos-dev
Sandboxed runtime for durable, observable AI agent workflows
TypeScript
Updated Feb 24, 2026
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What It Does
Provides a sandboxed runtime for running AI agents with durable workflows, automatic retries, and prompt caching. Uses built-in tools, human-in-the-loop approvals, and Slack integration to keep long-running agent tasks observable and controllable. Distinctive features include sandboxed execution environments and durable workflows so agents can be retried or paused without losing context.
Why It Matters
As agents act autonomously, being able to safely run, observe, and intervene in their workflows is essential to building trust. Polos gives teams the infrastructure to contain risky actions, require human approvals, and persist execution state—so agent failures become diagnosable and repeatable. That makes it easier to collect agent track records and other trust signals needed for evaluation and reputation systems. Responsible AI
Best For
Teams building agentic applications that need safe execution, human approvals, and Agent Service Mesh Pattern in development or early production.
How It's Used
- Run untrusted agent code safely in a sandboxed environment before production rollout
- Add human approval gates to high-risk agent actions and pause/resume workflows
- Persist long-running agent workflows with automatic retries and prompt caching for reproducible failure analysis
Works With
slacktypescriptpython
Topics
agent-orchestrationagentic-aiai-agentsai-observabilitydeveloper-toolsdurable-executionhuman-in-the-looppythonsandboxtypescript
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Keywords
agent reliabilitydurable-executionagent-to-agent evaluationhuman-in-the-loop