Naut

Provides a graphical interface for constructing agent workflows by connecting nodes representing tasks, decision points, and tool integrations. The builder likely uses a directed acyclic graph (DAG) execution model where nodes represent discrete operations and edges define control flow, enabling non-technical users to orchestrate multi-step agent behaviors without writing code.

Executes constructed agent workflows by orchestrating sequential or parallel task execution, managing state between steps, and invoking external tools or APIs based on agent decisions. The runtime likely implements a step-by-step execution loop that evaluates conditions, calls tools, processes results, and updates context for subsequent steps.

Manages a registry of available tools and external APIs that agents can invoke, likely using schema definitions (OpenAPI, JSON Schema) to describe tool inputs, outputs, and behavior. The system probably auto-generates UI components for tool configuration and validates tool calls against schemas before execution.

Provides managed hosting and deployment infrastructure for agents, likely handling containerization, scaling, and lifecycle management. The platform probably abstracts away infrastructure concerns and provides deployment endpoints (HTTP APIs, webhooks, scheduled triggers) for invoking agents without users managing servers.

Provides visibility into agent execution through structured logging, execution traces, and performance metrics. The system likely captures each step of agent execution, tool invocations, and decision points, enabling debugging and optimization of agent behavior.

Allows customization of agent behavior through prompt engineering, system instructions, and parameter tuning. Users likely define how the agent should reason, what tone or style to use, and how to handle edge cases through natural language prompts or configuration parameters.