> ## Documentation Index
> Fetch the complete documentation index at: https://docs.alpha.isaree.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Build an agent
> Build an Agent on the Community Hub that you and other clinicians can use from Patient Chat or Workspace in Isa.
This guide walks through building an [Agent](/concepts/agent) end-to-end on the [Community Hub](https://alpha.isaree.ai/en/community). By the end you'll have a published Agent that you and other clinicians can download to Isa and use from [Patient Chat](/isa/patient-chat) or [Workspace](/isa/workspace).
You have two starting points: build a new Agent from scratch, or duplicate an existing Agent on the Community Hub and adapt it. This guide walks through building from scratch — duplicating uses the same form, pre-filled with the original Agent's settings.
## Prerequisites
* A Community Hub account
* A clear idea of what the Agent should do — its role, the model it should run on, and the surfaces it should be available in
## Open the build form
Sign in to the [Community Hub](https://alpha.isaree.ai/en/community) and start a new Agent from the **Build** menu in the top navigation. The form opens with the Agent's fields ready to fill in.
## Name your Agent
Names are how other clinicians find your Agent in the [Isa Hub](/isa/browse-hub) — prefer something concrete like `Discharge Summary Drafter` over `My Agent`.
## Pick a model
Set your device — iPhone, iPad, or Mac — and the model picker tailors every recommendation to it. [LLMs and VLMs](/concepts/llms-vlms) live in one unified list: each model shows how much [RAM](/concepts/ram-device-memory) it needs and a **fits your device** badge, models that fit float to the top, and ones that don't are dimmed. Start typing to search Hugging Face directly — results show the organization, task, license, and usage stats inline, and carry the same **Runs / Needs X GB** badge as the curated models.
The RAM estimates are deliberately conservative — they guarantee the model runs well on your device with a full context window, not that it barely loads. See [Choose a model](/guides/choose-a-model) for the reasoning.
Below the model field sits a **Thinking** toggle, currently locked to disabled: in our testing, step-by-step reasoning makes [tool calling](/concepts/tool-calling) much less reliable, so Agents run with thinking off where the model allows it.
## Describe the intended use
The **Intended Use** field is what other clinicians read when they find your Agent in the Isa Hub. One or two sentences on what the Agent does and when to use it is enough.
## Pick a category and environments
* **Category** — Admin or Clinical.
* **Environments** — pick where the Agent shows up in Isa. Workspace is selected by default; also select **Patient** if it should be available in [Patient Chat](/isa/patient-chat).
The Environment decides which picker the Agent lands in on the device. An Agent that needs a patient's chat history to do its job — a discharge-summary drafter, a visit summariser — belongs in **Patient**. A general-purpose assistant the clinician opens to draft, brainstorm, or look something up without a specific patient in mind fits **Workspace**. Select both if the Agent is genuinely useful in both. Downloading an Agent in Isa won't surface it in a picker whose Environment the author didn't select.
## Write the system prompt
The System Prompt defines how the Agent behaves — its role, its tone, the format of its output, and the constraints it operates under.
For a structured framework, see [Create a system prompt](/guides/create-a-system-prompt).
## Add slash commands
Slash Commands are pre-written prompts attached to the Agent. Each command has a name, a short description, and the prompt that gets sent to the Agent when invoked — typing `/summarize-visit` expands into the full prompt behind it.
They do two things at once: save the user from retyping prompts they send again and again, and let you — as the Agent author — package the specific instructions you've tested with this model and system prompt. When another clinician downloads the Agent, they get your slash commands too, so the prompts you've tuned travel with the Agent.
A starter command is created for you. Add more for distinct tasks the same Agent handles — up to 20 per Agent — and remove the starter if you don't need it.
Use action-oriented names: `/summarize-visit` reads better than `/summary`.
## Publish the Agent
Once Agent Name, Model/Repository, Category, Environments, and System Prompt are filled in, publish the Agent. It now exists on the Community Hub and is ready to download from the [Isa Hub](/isa/browse-hub).
## Attach a Knowledge Base
A Knowledge Base is a set of documents you attach to the Agent so it can answer grounded in content the model didn't see during training. Local guidelines, protocols, references, internal documents — anything the Agent should be able to search and reason over while you chat with it.
To attach one, edit the Agent again after you publish it — the Knowledge Base section unlocks once the Agent exists. Upload the files the Agent should be able to search during conversations. Anyone who downloads your Agent in Isa pulls down these documents alongside it. A Knowledge Base is currently capped at 7 documents, 30 pages each.
Uploading takes a while in alpha: each PDF is converted into Markdown in the background — a format Agents consume more reliably — and the upload only completes once that conversion finishes.
Knowledge Base is an alpha feature. Extraction quality is shaped by several interacting factors:
* **The Agent's model** — how well a model uses a Knowledge Base depends on its [tool-calling](/concepts/tool-calling) quality, size, reasoning capability, and more. No single trait predicts performance.
* **The system prompt** — it influences whether and when the Agent reaches for its documents, alongside other factors that are outside your control.
* **File names** — descriptive titles like `discharge_protocol_v2_20260512` help the Agent pick the right document over `scan_3.pdf`.
* **PDF → Markdown conversion** — clean, text-heavy PDFs convert reliably; documents with complex layouts (multi-column pages, tables, embedded images, scans) convert worse and degrade what the model can read.
Because of that stack, the same Knowledge Base can produce good answers on one document and miss on another.
Always check what the Agent produces against the source document.
## Next
How the picker sizes models to your device — and why its recommendations look conservative.
A structured framework for writing reliable, clinical-grade system prompts.
Build an Agent that transcribes, diarizes, and structures a consultation.