diff --git a/docs/getting-started/overview.mdx b/docs/getting-started/overview.mdx
index a01bb9913..5a0d18ed7 100644
--- a/docs/getting-started/overview.mdx
+++ b/docs/getting-started/overview.mdx
@@ -3,3 +3,75 @@ title: "Overview"
description: "Why Arc?"
---
+Arc is the software factory for small teams of expert engineers. It replaces the prompt-wait-review loop with version-controlled workflow graphs that orchestrate AI agents, shell commands, and human decisions into repeatable, long-horizon coding processes.
+
+## The problem
+
+AI coding agents have transformed software engineering productivity, but the surrounding toolchain hasn't kept up:
+
+- **Developers work for the agents.** The prompt-wait-review loop idles engineers while agents run, then demands constant babysitting to course-correct.
+- **Unpredictable agents force oversight.** Non-deterministic guardrails create an explosion of failure modes. Engineers compensate by watching every step.
+- **Verification is overwhelmed.** Agent throughput exceeds human review capacity. CI pipelines designed for pass/fail signals can't keep pace with the volume or nuance of AI-generated code.
+- **Token costs are primed to explode.** ROI per token diverges wildly across tasks, models, and harnesses. Every unnecessary frontier token is one that can't be spent where it matters.
+- **The continuous improvement loop broke.** Data is lost at every sub-process boundary. Organizations can't train LLMs the way they train people, and memory files make no guarantees.
+
+## How Arc solves this
+
+Arc gives you a deterministic harness around non-deterministic AI. You define **workflow graphs** in DOT files that specify exactly what happens, in what order, with which models, and where humans weigh in. Arc handles orchestration, parallelism, model routing, verification, and observability.
+
+
+
+ Define workflows as code in Graphviz DOT. Nodes are agents, shell commands, or human input gates. Fan out, loop, branch, and resume — all traceable and repeatable.
+
+
+ Route tasks to the right model using CSS-like stylesheets. Cross-critique with fresh eyes, delegate simple tasks to fast models, and fail over automatically when providers go down.
+
+
+ Steer while the agent runs, not after. Approval gates, interviews, and steering let you intervene at the right moments without waiting for a pull request.
+
+
+ Combine LLM-as-judge, test suites, third-party tools, and human review. Verifications act as an eval suite tailored to your organization, building confidence over time.
+
+
+ Every tool call, agent turn, and shell command is captured in a unified event stream. Query run data with SQL via DuckDB and generate automatic retrospectives.
+
+
+ Licensed under AGPL. Written in Rust with minimal dependencies. Runs on a single node with no databases to set up.
+
+
+
+## What a workflow looks like
+
+Workflows are defined in Graphviz DOT, a simple graph description language:
+
+```dot
+digraph PlanImplement {
+ graph [goal="Plan, approve, implement, and simplify a change"]
+
+ start [shape=Mdiamond, label="Start"]
+ exit [shape=Msquare, label="Exit"]
+
+ plan [label="Plan", prompt="Analyze the goal and codebase. Write a step-by-step plan.", reasoning_effort="high"]
+ approve [shape=hexagon, label="Approve Plan"]
+ implement [label="Implement", prompt="Read plan.md and implement every step."]
+ simplify [label="Simplify", prompt="Review the changes for clarity and correctness."]
+
+ start -> plan -> approve
+ approve -> implement [label="[A] Approve"]
+ approve -> plan [label="[R] Revise"]
+ implement -> simplify -> exit
+}
+```
+
+This workflow plans a change, asks a human to approve it, implements the plan, and simplifies the result. If the human rejects the plan, the agent revises it. The entire process is version-controlled, repeatable, and resumable.
+
+## Next steps
+
+
+ Install Arc and run your first workflow in minutes.
+