For AI help writing code inside a Workspace, see coding assistants instead. Those assist you as you work; agents are systems you build and deploy for others to use.
How an agent reaches its users
An agent runs on Domino the way an App does. You deploy it either from the Experiment Manager, directly from an evaluated agent version, or from your Project’s Apps view using the standard publish an App workflow. Once it’s running, it appears alongside Apps under Deployments > Apps & Agents, where the people you’ve shared it with can find it and use it in the browser with nothing to install. Because deployment reuses the App path, agents get the platform’s hosting, permissions, and routing without extra setup. Agents also have their own audit trail events, covering access granted and revoked, access requests, and changes to an agent’s visibility and discoverability.How it works in Domino
A trace is a structured record of every LLM call, tool invocation, and decision your agent makes. It captures token usage, latency, and cost. One decorator (@add_tracing) instruments your code, and the same trace data flows through every phase:
1
Set up LLM access
Connect to an external LLM provider (OpenAI, Anthropic, Bedrock, or Azure OpenAI) or to a Domino-hosted endpoint, and store credentials as Domino environment variables. Set up LLM access covers both approaches.
2
Develop in a Workspace
Write your agent code using any framework and add
@add_tracing with inline evaluators to instrument it. Then prepare test data for your agent configuration. See Develop agentic systems.3
Evaluate as a Job
Run your evaluation script as a Domino Job. Each Job creates an agent version in the Experiment Manager, capturing traces with evaluation scores for that configuration. Runs aggregate evaluation results across all traces.
4
Compare and deploy
In the Experiment Manager, compare runs and individual traces within runs, then deploy from any agent version that originated from a Job. Domino tracks lineage between development and production, including the commit, the agent configuration, and the performance results.
5
Monitor in production
The same
@add_tracing instrumentation captures live user interactions as traces in the Agent Dashboard. Schedule evaluation Jobs to score production traces continuously and iterate when quality drops.Start from a working example
Clone one of these repositories to see the whole workflow running before you instrument your own code:simple_domino_agent
Minimal agent with tool calls, tracing, evaluation, and deployment.
rag-agent-demo
Retrieval-augmented generation (RAG) agent with ChromaDB, using the same Domino instrumentation patterns.
simple_agent_api_only
REST API and agent-to-agent (A2A) example, with instructions for pairing it with a Domino-hosted agent registry and orchestrator.
Related
- Agentic AI overview: conceptual background on what makes a system agentic, and where a given design sits on the workflow-to-agent spectrum.
- Set up LLM access: choose between an external provider and a model you host in Domino.
- Publish Apps: the deployment path agents share with Apps.