Harden an existing agent or team by finding weaknesses through realistic user simulations, diagnosing their causes, making targeted improvements and verifying more reliable behavior. Use for agent hardening, failing evals, simulation-driven improvement, production-trace investigation or turning useful scenarios into evals. Also support simulation-only requests. Use create-evals for coverage authoring alone and extend-agent for a specified capability change.
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Create and register a code-defined agent in this AgentOS from a concrete request or guided discovery, add its UI prompts, reload the development runtime, and verify a real run. Use when the user wants to build or add an agent.
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Author eval coverage for an agent in this AgentOS — map what the agent promises, mine real sessions and eval history from Postgres for scenarios, propose capabilities worth testing, then write, run, and audit Case entries in evals/cases.py. Use when the user wants evals created, coverage added, or an agent's behavior pinned down as tests. For failing suites or agent hardening, use improve instead.
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Deploy this AgentOS using its own provider instructions, configure JWT verification, and verify the public service. Use for first deployment, redeployment, or production configuration changes.
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User-driven loop to change an existing agent in this AgentOS — add a tool/MCP server/toolkit, add a capability (knowledge base, learning/memory, sub-agent, scheduled task), grow the safe Studio registry so components built at runtime gain a new capability, refine its instructions, or fix a specific known bug, verifying each change against the selected development runtime. Use whenever the user names a concrete change to an agent, or wants a new building block available to the platform. For agent hardening without a specific change in mind, use improve.
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Review this AgentOS repository for public readiness after a refactor or before release. Compare documentation, skills, configuration, component wiring and scripts with the implementation; fix clear documentation drift when requested and report behavioral issues with evidence. Use for a repository consistency sweep, not agent-quality tuning or deployment.
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Set up this AgentOS from a fresh clone — confirm Docker, configure .env, boot the containers, prove the MCP endpoint live, connect the AgentOS UI, then build the user's first agent. Use when the user asks to set up the platform, get started, or bring this repo up on a new machine.
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