Agent infrastructure / Jan 2026 - Mar 2026
A2A Multi-Agent Builder
A platform for dynamically creating, configuring, and orchestrating specialized multi-agent AI teams without hardcoding each agent workflow into the application.
A2A / TypeScript / MongoDB Atlas / Agent Orchestration / Prompt Versioning
Highlights
- Built an agent factory that assembles specialized agent teams at runtime from stored configurations.
- Supported both parallel and sequential execution with structured aggregation of agent outputs.
- Centralized agent definitions, prompts, routing logic, and shared context instead of embedding them directly in application code.
- Added versioning and rollback for prompts and agent configurations using MongoDB Atlas.
- Stored execution information and configuration history to make agent behavior easier to inspect and reproduce.
Problem
Multi-agent systems become difficult to evolve when agent definitions, prompts, routing rules, and execution order are hardcoded directly into the application.
Adding a new specialist can require changing orchestration code. Updating a prompt can change system behavior without an easy way to compare versions or roll back. Different workflows may also require different combinations of agents even when they share the same underlying infrastructure.
I wanted to separate what an agent is from how the application is coded, so agent teams could be assembled and changed dynamically.
What I built
I built a configuration-driven system for creating and running specialized groups of agents.
The core of the platform is an agent factory. Instead of defining every agent directly inside a workflow, the system loads agent configurations and prompts and assembles the required team at runtime.
Each agent can have its own role, instructions, configuration, and place within the larger workflow.
The orchestration layer supports both:
- Parallel execution, when multiple specialists can work independently.
- Sequential execution, when one agent's result becomes context for the next.
Their outputs are then collected into structured responses for the calling workflow.
MongoDB Atlas stores the system's prompts, agent configurations, routing information, shared context, execution data, and configuration history.
How it works
A workflow begins by defining what kind of agent team it needs.
- The system retrieves the relevant agent and prompt configurations.
- The agent factory creates the required specialized agents at runtime.
- The orchestration layer determines whether those agents should execute in parallel, sequentially, or through a combination of both.
- Agents receive the context required for their part of the task.
- Their responses are collected and normalized into structured results.
- Shared information can be passed between agents as the workflow progresses.
- Configuration and prompt versions are retained so changes can be tracked or rolled back.
The result is an orchestration system where changing the composition or behavior of an agent team does not necessarily require rewriting the underlying workflow implementation.
Technical focus
The main design decision was to treat agent behavior as configuration rather than application structure.
That meant separating several concerns:
Agent creation
Which specialists should exist for this workflow?
Execution
Which agents can run independently, and which depend on previous results?
Context
What information needs to be shared between agents?
Configuration
Which prompts, routing rules, and agent settings produced this behavior?
Versioning
Can a previous working configuration be restored when a change performs worse?
That architecture made the system less about one specific multi-agent workflow and more about the infrastructure required to create different teams on top of the same orchestration layer.