Building Agents in Studio

Create, configure, test, and manage agents using the full Studio Builder.

Use the Studio Builder when you need full control over an agent’s model, prompt, features, tools, knowledge base, and deployment settings. If you want to describe your agent in plain English first and let Studio generate an initial configuration, start with the Conversational Builder and switch to Studio for fine-tuning.

Create an agent

Navigate to Agents in the sidebar and select New Agent.

Required fields

Name is the internal identifier shown in the agents list and in API responses.

Model is the LLM provider and model. Select based on latency, cost, reasoning depth, and compliance requirements.

Role sets the agent’s domain and persona. A specific role produces better output than a generic one.

Goal is the agent’s high-level objective. Keep it to one clear sentence.

Instructions define behavior, output format, edge cases, constraints, and step-by-step logic.

Optional features

Feature	Config options	Notes
Memory	Cognis, Lyzr Memory, or Bedrock AgentCore	Cognis is recommended for new agents
Knowledge Base	Select one or more KBs	Classic, Graph, or Semantic
Tools	Pre-built, custom, or MCP tools	Choose shared or per-user auth
Skills	Built-in or uploaded skills	Adds reusable multi-step logic
Responsible AI	Select a guardrail policy	Policies are created in the Responsible AI section
Global Context	Select a context document	Injects org-wide instructions without per-agent duplication
Structured Output	Define a JSON schema	Forces machine-readable output
Data Query	Select a Semantic Model	Enables natural-language-to-SQL queries
Scheduler	Configure a time trigger	Use for recurring agent runs
Webhook	Configure an event trigger	Use for event-driven runs

Test your agent in the Playground

The Playground lets you send messages, upload files, and iterate on instructions before deployment. Cover the following scenarios before going to production:

• The primary use case with a typical user input
• Ambiguous or underspecified inputs to check graceful handling
• Requests the agent should decline, such as unsafe or out-of-scope queries
• Tool calls: verify the agent selects the correct tool and passes valid arguments
• Knowledge retrieval: verify the agent returns grounded, cited answers from the connected KB

Save and versions

Every save creates a new version snapshot. You can review and restore previous versions from the Version History panel, accessible via the history icon in the top-right of the agent builder.

Deploy your agent

Once you are satisfied with the Playground results, select Deploy in the top navigation. The Deploy tab confirms the agent is live and gives you everything you need to integrate it.

The Deploy tab provides three tabs for integration:

• Agent API: The primary cURL command for calling your agent. Copy it and paste it into your app, site, or backend using the request format shown.
• Agent JSON: The full agent configuration as JSON, for importing or version-controlling the agent definition.
• How to use: Inline guidance on passing user input and rendering the response.

The three-step integration flow is: copy the cURL from the Agent API tab, drop it into your application code, then pass user input and render the response.

To publish the agent to the Lyzr App Store without writing integration code, select Publish from the banner on the Deploy tab.

Update an agent

Select the agent from the Agents list to open its configuration. Modify any field and select Update to apply the changes. Re-run tests in the Playground to confirm the updates produce the intended behavior.

Next steps

• Connect a Knowledge Base
• Configure tools
• Set up Responsible AI guardrails
• Evaluate your agent