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review-and-improve

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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Review and Improve

Review the requested scope against AGENTS.md and the actual implementation. When comparing with a branch or PR, record the base and head SHAs, and distinguish committed changes from local edits. A review-only request stays read-only. For a review-and-improve request, correct clear documentation and link errors within scope; report behavior changes separately unless the user has authorized their repair.

1. Establish the scope

Inspect the working tree before editing and preserve concurrent changes. Review the named files first. A repository-wide pass covers README.md, AGENTS.md, example.env, skills, app/, agents/, teams/, db/, evals/, scripts/, and build/deployment configuration. Include user-added workflows or other directories when present. Exclude environments, caches, generated files and Git internals from content scans. Do not print env-file values.

A running API is not required for the source review. Establish whether development uses Compose or the host API before attempting runtime checks; use the matching README commands. If a runtime is unavailable or belongs to another checkout, continue the source review and report the runtime verification gap.

2. Compare declarations, documentation and behavior

CheckEvidence
Application agents and teams are deliberately registered, with stable IDsSource declarations, app/main.py, app/config.yaml
Framework-owned components stay in AgnoOSManager(...), CompanyConfig(...), installed framework source
Shared resources and Studio capabilities match documentationapp/registry.py, db/, AgentOS registration
Configuration names, defaults and prerequisites agreeEnvironment reads, example.env, README, Compose, deployment scripts
Documented commands and file paths workScripts, skills, CLI help and installed signatures
Skills remain discoverable and their routing is coherentFrontmatter, relative links, .claude/skills, CLAUDE.md
Eval targets, tool names and CLI examples existevals/cases.py, evals/__main__.py, installed Agno eval APIs
Sandbox documentation matches execution and persistence requirementsapp/sandbox.py, agents/engineers.py, Dockerfile, README
MCP claims match the exposed interface and authentication behaviorapp/interfaces.py, app/main.py, scripts/mcp_check.sh

Do not register every file found under agents/ or invent manifest entries for internal components: determine whether it is an intended public component, a helper or framework-owned. OS Manager, its members and Company Manager are framework-owned; do not restore the old application-owned manager/builder/engineer stack. OS, Company and queue storage reuse get_postgres_db(); knowledge content adapters have distinct IDs.

app/company.py declares agentos-maintenance: Company Manager reviews releases and the codebase every day at 09:00 UTC, proposes up to three Backlog Tasks and has automatic acceptance disabled. Verify the declaration against saved schedule state; do not infer successful execution from its presence. Before removing or renaming a component in an existing deployment, inspect saved schedules and Studio references read-only when access is available. Source absence does not establish that a saved record is absent. Preserve historical sessions, notes and Company records.

3. Verify proportionately

Use static checks first:

source .venv/bin/activate
./scripts/validate.sh
git diff --check

For shell changes, run bash -n on the affected scripts. After authorized edits, format only as needed and inspect the resulting diff. Use disposable local fixtures to reproduce script failures when useful; keep the starter free of a bundled regression-test suite. CLI help and mock fixtures do not establish successful cloud deployment.

For an available local API, verify /health and the relevant component listing using its configured authentication. Confirm the process or container serves this checkout before drawing conclusions. Run scripts/mcp_check.sh in Compose mode, or MCP_CHECK_HOST=1 ./scripts/mcp_check.sh for the host API. The default MCP check proves initialization and required tool availability, not an agent answer.

Run model probes or evals only within the user's authorized scope and budget. Inspect available cases before selecting a tag; do not assume release or live cases exist. Even eval CLI help/list commands import knowledge storage and require PostgreSQL; inspect evals/cases.py directly when the database is unavailable. Evals use the application's database and persist sessions and results. Use explicitly owned fixtures for mutating cases; do not restore snapshot-diff cleanup or delete historical data. Do not smoke every engineer automatically: those runs can execute code and modify workspaces. HTTP availability, direct execution, delegation and Company Task completion are separate checks.

4. Report

Lead with actionable findings: severity, file and line, triggering condition, observed or source-backed consequence, and recommended fix. Label inherited issues separately from regressions against the comparison base. Explain what was corrected and what remains. Give a sharing or merge recommendation limited to the reviewed scope.

Record verification in tmp/VERIFY.md when making changes, separating static, deterministic, model, container and hosted results. Report skipped checks and their limits; do not equate a passing linter or mock fixture with a healthy deployed platform.

Route agent behavior changes to extend-agent, missing eval coverage to create-evals, eval failures and agent hardening to eval-and-improve, and an explicitly requested deployment to deploy-platform.

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같은 저장소의 스킬

Add a new agent to this AgentOS. Runs guided discovery or takes a concrete idea, then generates agents/slug.py, registers it in app/main.py, adds its manifest entry (description + quick prompts), reloads the selected development runtime, and smoke-tests it for you. Covers the product agent too — ingest a product's website/docs into the product knowledge base and build a knowledge-only agent over it. Use whenever the user wants to add or create a new agent, or names a product (theirs or one they use) they want an agent for.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agno-agi/agentos-render52026년 10월 25일 업데이트

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 exploratory simulation-driven hardening, use eval-and-improve instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agno-agi/agentos-render52026년 10월 25일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agno-agi/agentos-render52026년 10월 25일 업데이트

Evaluate and improve an existing AgentOS agent or team. Repair failing eval cases, or explore behavior by generating simulated user scenarios from instructions and sanitized usage, running probes, inspecting evidence and iterating within a bounded budget. Use for failing evals, autonomous hardening or simulation-driven improvement. Use create-evals for coverage authoring alone and extend-agent for a requested new capability.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agno-agi/agentos-render52026년 10월 25일 업데이트

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 autonomous hardening with no specific change in mind, use eval-and-improve.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agno-agi/agentos-render52026년 10월 25일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agno-agi/agentos-render52026년 10월 25일 업데이트

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