> ## Documentation Index > Fetch the complete documentation index at: https://docs.agents-squads.com/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > AI systems you can learn, understand & trust ## Welcome to Agents Squads We build transparent AI systems that teams can learn, understand, and own — not black boxes, but reliable systems earning trust through consistent results. Get up and running in minutes Understand agents, squads, and memory Integrate with your systems See real implementations ## Why Agents Squads? Trust is the bottleneck, not capability. Most AI systems fail not because they can't perform — but because teams can't understand, modify, or trust them. We take a different approach: * **Transparent** — See exactly what agents do and why * **Learnable** — Your team can understand and modify the system * **Ownable** — No vendor lock-in, you control your agents * **Reliable** — Consistent results you can depend on ## How It Works Agents Squads organizes AI capabilities into domain-aligned teams — and closes the loop: every cycle's output is evaluated and fed back into the next. `squads init` ships four starter squads: | Squad | Focus | Agents | | ---------------- | --------------------------------------------------------- | ------------------------------------ | | **intelligence** | Strategic synthesis — Know / Don't Know / Playbook briefs | intel-lead, intel-eval, intel-critic | | **research** | Market, competitor, and trend research | lead, analyst, synthesizer | | **product** | Roadmap, specs, feedback synthesis | lead, scanner, worker | | **company** | Evaluates every squad's output, closes the feedback loop | manager, evaluator, goal-tracker | ### The execution loop The agent execution loop — context, execution cycle, human gate, outcomes The agent execution loop — context, execution cycle, human gate, outcomes Every run moves through the same four zones. **Context**: the run assembles only the minimal slice its task needs — agent definition, priorities, relevant squad memory, and typed views over your data. **Execution cycle**: the agent reasons, calls scoped schema-checked tools, and renders its output for review. **Human in the loop**: you approve, reject, edit, or escalate — nothing commits without the gate. **Outcomes**: external work product lands (data writes, artifacts) and internal memory accumulates (state, learnings) — so the next cycle starts richer. See [Architecture](https://github.com/agents-squads/squads-cli/blob/main/docs/architecture.md) for how the underlying context cascade works.