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2 changes: 0 additions & 2 deletions docs/README.md
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title: "Perstack: The Agent Runtime"
---

# Perstack: The Agent Runtime

Define AI agents as declarative **Experts** in TOML. Execute them with deterministic, event-derived tracking. Each Expert runs in its own isolated context — no shared state, no prompt bloat, full execution history.

- [Getting Started →](./getting-started.md)
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2 changes: 0 additions & 2 deletions docs/contributing/roadmap.md
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title: "Roadmap"
---

# Roadmap

This roadmap outlines the direction of Perstack development. Priorities may shift based on community feedback and emerging requirements.

> [!WARNING]
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2 changes: 0 additions & 2 deletions docs/getting-started.md
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title: "Getting Started"
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# Getting Started

This walkthrough takes you from zero to production integration. Each step addresses one or more of the [five structural problems](../README.md#why-perstack) in agentic app development.

## Prerequisites
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2 changes: 0 additions & 2 deletions docs/guides/README.md
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title: "Working with Perstack"
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# Working with Perstack

What are you trying to do?

| I want to... | Start here |
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2 changes: 0 additions & 2 deletions docs/guides/adding-ai-to-your-app.md
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order: 2
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# Adding AI to Your App

You have an existing application — maybe a web app, a CLI tool, or a backend service. You want to add AI capabilities, but you're not sure where to start.

**The challenge**: Agent frameworks often want to own the entire application. They're designed for building agent-first apps, not for embedding AI into existing systems.
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2 changes: 0 additions & 2 deletions docs/guides/extending-with-tools.md
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order: 3
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# Extending with Tools

Your agent can reason and generate text. But sometimes that's not enough — you need it to search the web, query a database, or call an API.

**The problem**: Integrating external tools usually means writing code, managing connections, and handling errors. Each new tool is a custom integration.
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2 changes: 0 additions & 2 deletions docs/guides/going-to-production.md
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order: 5
---

# Going to Production

Your agent works in development. Now you want to deploy it for real users.

**The concern**: AI agents have larger attack surfaces than typical applications. They make decisions, use tools, and interact with external systems. How do you deploy them safely?
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2 changes: 0 additions & 2 deletions docs/guides/rapid-prototyping.md
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order: 1
---

# Prototyping for Agent-First Apps

When you build an agent-powered app, the instinct is to start with the app — set up a project, install dependencies, write scaffolding. Then somewhere in the middle, you start figuring out what the agent should actually do.

This is backwards.
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2 changes: 0 additions & 2 deletions docs/guides/taming-prompt-sprawl.md
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order: 4
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# Taming Prompt Sprawl

Your agent started simple. A focused prompt, clear behavior, reliable results.

Then requirements grew. You added more capabilities, more edge cases, more instructions. Now your prompt is 50,000 tokens long — a small novel — and the agent gets confused between tasks. You're afraid to change anything because you might break something else.
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2 changes: 0 additions & 2 deletions docs/making-experts/README.md
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title: "Making Experts"
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# Making Experts

Experts are defined in `perstack.toml`. This section covers how to define, test, and publish Experts.

## The essentials
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2 changes: 0 additions & 2 deletions docs/making-experts/base-skill.md
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title: "Base Skill"
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# Base Skill

Every Expert automatically has access to `@perstack/base` — a built-in skill that provides file operations, runtime control, and other essential tools.

**No configuration needed. It's always available.**
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2 changes: 0 additions & 2 deletions docs/making-experts/best-practices.md
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title: "Best Practices"
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# Best Practices

These principles help you avoid common pitfalls in agent development: monoliths, complexity explosions, debugging nightmares, and fragile systems. Building a large agent head-on almost always fails.

> [!NOTE]
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2 changes: 0 additions & 2 deletions docs/making-experts/examples.md
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title: "Examples"
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# Examples

Patterns for defining Experts. Each example highlights a specific skill type or integration approach.

- [GitHub Issue Bot](#github-issue-bot)
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2 changes: 0 additions & 2 deletions docs/making-experts/publishing.md
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title: "Publishing Experts"
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# Publishing Experts

Share your Experts with the community by publishing them to the Perstack Registry.

## Before publishing
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2 changes: 0 additions & 2 deletions docs/making-experts/skills.md
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title: "Skills"
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# Skills

Skills give Experts access to external tools via MCP (Model Context Protocol).

| Type | Use case |
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2 changes: 0 additions & 2 deletions docs/making-experts/testing.md
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title: "Testing Experts"
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# Testing Experts

Experts are probabilistic — the same query can produce different results. This guide covers strategies for testing effectively.

## Local testing
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2 changes: 0 additions & 2 deletions docs/operating-experts/deployment.md
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title: "Deployment"
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# Deployment

Perstack runs on any Node.js environment. Choose your deployment target based on security requirements, cost, and operational preferences.

## Docker
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2 changes: 0 additions & 2 deletions docs/operating-experts/isolation-by-design.md
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title: "Isolation by Design"
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# Isolation by Design

Perstack is designed to run inside isolated environments. The runtime itself doesn't enforce security boundaries — your infrastructure does.

For the rationale behind this approach, see [Sandbox Integration](../understanding-perstack/sandbox-integration.md).
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2 changes: 0 additions & 2 deletions docs/operating-experts/observing.md
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title: "Observing"
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# Observing

Every Perstack execution produces a complete audit trail. Use events and checkpoints to monitor, debug, and verify Expert behavior.

## Event stream
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2 changes: 0 additions & 2 deletions docs/operating-experts/skill-management.md
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title: "Skill Management"
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# Skill Management

The Perstack runtime manages three types of skills through specialized Skill Managers. Each type has different initialization behavior and security implications.

## Skill Types
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2 changes: 0 additions & 2 deletions docs/references/cli.md
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title: "CLI Reference"
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# CLI Reference

## Running Experts

### `perstack start`
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2 changes: 0 additions & 2 deletions docs/references/events.md
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title: "Events Reference"
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# Events Reference

Perstack emits events during Expert execution for observability and state management. This document explains the event system architecture and how to consume events.

## Event Types Overview
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2 changes: 0 additions & 2 deletions docs/references/perstack-toml.md
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title: "perstack.toml Reference"
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# perstack.toml Reference

- [Complete Example](#complete-example)
- [Runtime Configuration](#runtime-configuration)
- [Expert Definition](#expert-definition)
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2 changes: 0 additions & 2 deletions docs/references/providers-and-models.md
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title: "Providers and Models"
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# Providers and Models

Perstack supports multiple LLM providers. Configure via CLI options, environment variables, or `perstack.toml`.

## Default Model
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2 changes: 0 additions & 2 deletions docs/understanding-perstack/boundary-model.md
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title: "The Boundary Model"
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# The Boundary Model

[Isolation](./concept.md#isolation) is one of Perstack's three core goals. Experts are isolated from everything except their role — model access, context, tools, and dependencies are all mediated by the runtime.

The **boundary model** extends this isolation to the application architecture level: **separate the application from the agent runtime**.
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2 changes: 0 additions & 2 deletions docs/understanding-perstack/concept.md
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title: "Concept"
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# Concept

Perstack is built on a concept called **Expert Stack** — the architecture that enables agent-first development.

> [!NOTE]
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2 changes: 0 additions & 2 deletions docs/understanding-perstack/experts.md
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title: "Experts"
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# Experts

Experts are the core building block of Perstack — modular micro-agents designed for reuse.

> [!NOTE]
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2 changes: 0 additions & 2 deletions docs/understanding-perstack/registry.md
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title: "Registry"
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# Registry

The Perstack Registry is a community-driven ecosystem for sharing Experts — like npm, but for AI agents.

## Definition-only
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2 changes: 0 additions & 2 deletions docs/understanding-perstack/runtime.md
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title: "Runtime"
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# Runtime

The Perstack runtime combines probabilistic LLM reasoning with deterministic state management — making agent execution predictable, reproducible, and auditable.

## Execution model
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2 changes: 0 additions & 2 deletions docs/understanding-perstack/sandbox-integration.md
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title: "Sandbox Integration"
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# Sandbox Integration

## Why sandbox-first?

AI agents differ fundamentally from traditional software. The same input can produce different outputs. Model updates can change behavior. Hallucinations can trigger destructive actions without any attacker involved.
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2 changes: 0 additions & 2 deletions docs/using-experts/README.md
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title: "Using Experts"
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# Using Experts

This section covers **running and integrating Experts** — CLI usage, state management, and error handling.

## In this section
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2 changes: 0 additions & 2 deletions docs/using-experts/error-handling.md
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title: "Error Handling"
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# Error Handling

Perstack is designed to recover from errors automatically when possible. Most errors are fed back to the LLM for self-correction rather than crashing the run.

## How errors are handled
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2 changes: 0 additions & 2 deletions docs/using-experts/running-experts.md
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title: "Running Experts"
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# Running Experts

Perstack provides two commands for running Experts: `start` and `run`.

## `start` vs `run`
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2 changes: 0 additions & 2 deletions docs/using-experts/state-management.md
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title: "State Management"
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# State Management

Perstack automatically saves execution state, enabling continuation and replay.

## Execution hierarchy
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2 changes: 0 additions & 2 deletions docs/using-experts/workspace.md
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title: "Workspace"
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# Workspace

The workspace is the isolated directory where Experts execute. All file operations are confined to this directory.

## Default workspace
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