Workflows for Agentic AI

Vertesia provides unparalleled reliability and resilience for your generative AI and agentic AI pipelines. It ensures that even complex, multi-step workflows, such as those executed by agents involving chained prompts, tool use, and iterative refinement, complete successfully. Vertesia automatically handles failures, retries, and state persistence, allowing developers to focus on optimizing AI models and agent logic rather than on implementing robust error handling or recovery mechanisms.

Key Benefits

Durable AI Orchestration: Vertesia supports long-running AI workflows, from minutes to days. It preserves the state of generative processes and agentic decisions, ensuring reliable resumption after system failures or network interruptions. This capability is critical for multi-stage content generation, complex agentic explorations, and other extended AI tasks.

Simplified AI Pipeline Development: Vertesia abstracts the complexities of distributed computing, enabling the design and execution of sophisticated Agentic AI workflows using straightforward code. This reduces development overhead associated with managing AI agent state or coordinating complex generative processes.

Full Observability of AI Execution: Vertesia provides comprehensive visibility into generative AI and agentic workflows. Built-in tools and a detailed event history offer real-time status updates and complete execution traces for each AI task. This facilitates debugging agent behavior, auditing generative outputs, and understanding overall AI pipeline flow.

Scalable AI Workloads: Designed for horizontal scaling, Vertesia efficiently manages thousands to millions of concurrent AI workflow executions, supporting both complex agent simulations and large-scale content generation.

Configuration Options

Vertesia offers two methods for configuring workflows, accommodating varying levels of complexity and customization:

JSON DSL for Rapid Configuration: For straightforward generative AI tasks or basic agentic sequences, a JSON Domain-Specific Language (DSL) is available. This allows developers to define workflow logic, model parameters, and conditional execution directly within a human-readable JSON format. This option is ideal for rapid iteration on prompt engineering, simple agent decision trees, or orchestrating direct model calls without requiring custom code.

Code-Based Workflows with Custom Docker Images: For advanced generative AI or agentic logic requiring deep customization, integration with external systems, or specialized libraries, Vertesia supports code-based workflows. Developers can implement workflow logic using the Typescript SDK and package it within a custom Docker image. This provides full control over the execution environment, dependencies, and runtime, enabling the deployment of highly sophisticated AI agents, advanced generative pipelines with custom pre/post-processing, or niche AI frameworks directly within the Vertesia workflow execution environment.