Not a feature dump — a way in to understanding agents
Three practical tracks: understand AI agents, inspect Berth's asset model, and use short guides when something in your local setup is unclear. Every article keeps sources visible.
Understand AI agents
In plain words and trusted sources: how an agent differs from a chat model, and its core capabilities.
- What is an AI agent?
A chatbot answers; an agent acts. Plain-language definition of an AI agent and how it differs from a chat model.
- The six core capabilities of an agent
Perception, reasoning & planning, tool use, memory, autonomous multi-step execution, and multi-agent collaboration — explained simply.
- Large model vs. agent: what actually changes
The same model, with or without an agent around it, behaves very differently. Here is the distinction that matters.
Berth features in depth
A walk through Overview, Sessions, Configuration and Usage — and the asset model behind them.
- The asset model: what Berth actually shows you
Berth turns the plain-text files behind your agents into structured, connected objects it calls assets. Here is the model.
- Overview & Sessions: see activity and history
The dashboard at a glance, and how to walk back through past sessions with the assets and tools each one used.
- Configuration · Instructions: memories, skills, subagents
The instruction assets that guide your agent — and how Berth shows their scope, imports, and where each one comes from.
- Configuration · Capabilities: MCP, hooks, permissions
The capability assets that give your agent power and set its boundaries — MCP servers, lifecycle hooks, and permissions.
- Usage, health checks & privacy
Cost and token trends, automated diagnostics, and the read-only / local-first guarantees behind it all.
Hands-on guides
Diagnose why a hook isn't firing, make sense of your cost, set a config baseline for your team.
- Why isn’t my hook firing?
A short checklist to diagnose a hook that never runs — using what Berth shows you.
- Make sense of your cost
Read Berth’s Usage screen to find what’s expensive and why — by model, project, and day.
- Set a config baseline for your team
Use scope and imports to give a team a shared, predictable agent setup — and verify it with health checks.