The Anatomy of an Agent
An agent is more than a model with a prompt. It is a stack of choices: which model does the reasoning, which harness runs the work, which capabilities the agent can use, and which context it can rely on.
Brainbase helps you manage that full stack. You can choose models, standardize harnesses, attach skills and tools, give agents durable context, and monitor whether the resulting system behaves the way your team expects.
Instead of scattering prompts, tools, evals, scripts, and context across separate systems, Brainbase gives each layer a home and shows how those choices affect real agent work.
The stack
| Layer | What it controls | Brainbase concept |
|---|---|---|
| Model | Reasoning, language quality, latency, cost, and modality support. | Models |
| Harness | The runtime, local environment, files, commands, and workflow conventions around the model. | Harnesses |
| Skills, tools, and MCP | The agent's portable expertise, integrations, functions, and external actions. | Skills and Tools |
| Context | The durable instructions, playbooks, memory, and business facts the agent can use. | Instructions, Playbooks, Memory |
Model
The model is the reasoning engine. It affects how well the agent follows instructions, writes, plans, uses tools, handles long context, processes images or files, and responds under latency or cost constraints.
Model choice matters, but it is only one layer. A stronger model can still fail if the harness is wrong, the tools are unclear, or the context is stale. A smaller model can perform well when the task is narrow and the surrounding agent design is strong.
Harness
The harness is the environment around the model. It determines how the agent receives work, what files or commands it can use, how it runs code, how it stores state locally, and which conventions it follows while doing the task.
Harnesses make agents practical. They turn a model into something that can work inside a real team environment instead of a blank chat box.
Skills, tools, and MCP
Capabilities are what let the agent do more than talk. Skills package reusable expertise. Tools and MCP servers connect the agent to systems, data, and controlled actions. Together, they let the agent inspect, transform, update, and coordinate work.
This layer should be deliberate. Give an agent the capabilities it needs for its job, not every capability available in the workspace.
Context
Context is the information the agent uses to decide what to do. Some context is always-on, like instructions. Some is retrieved when needed, like playbooks. Some is structured and persistent, like memory. Some is provided by the current conversation or request.
Good context makes the agent less generic. It tells the agent what matters to your team, what rules it should follow, which facts it can trust, and when it should ask for help.