Shared Agent Framework

Mis à jour le 2025-12-22Confiance : high

shared-frameworkpython-runtimeagent-infrastructurereusable-componentsalan-platformmo-assistantautomated-supportrapid-deploymentplatform-strategyconversation-managementtool-integration

Python-based agent runtime and framework at alan-health that powers multiple AI products including Mo (personalized medical assistant), automated support resolution, and operations agents. Enables rapid deployment of new conversational AI agents across different domains through reusable infrastructure components.

Architecture Components

Agent Base Class: Handles branching logic for loading agent configuration from remote Git branches, tool call validation workflows, and human-in-the-loop approval patterns.

Conversation Management: Shared backend code for associating agent conversations with arbitrary business objects, enabling integration across different operational contexts.

Tool Integration: Standardized tool calling interface with configurable permission levels and validation workflows, supporting both read-only automatic execution and write operations requiring human approval.

Runtime Environment: Production-ready Python runtime handling agent execution, state management, and integration with existing internal systems and databases.

Reusability Benefits

Rapid Bootstrap: New agent development reduced from weeks to days through shared infrastructure, demonstrated by quick deployment of Belgium claims agent and sister team's sales AI agent.

Consistent Patterns: Standardized approaches to common agent needs (conversation state, tool permissions, human-in-the-loop workflows) reducing cognitive overhead and maintenance complexity.

Cross-Team Enablement: Platform provides sufficient abstraction for other teams to deploy their own agents without deep AI engineering expertise, as demonstrated by sales team adoption.

Quality Inheritance: New agents inherit battle-tested patterns for security, reliability, and operational integration developed through production use across multiple domains.

Framework Features

Branch-Based Configuration: Integration with git-based-configuration allowing agents to load prompts and tool configurations from any Git branch for testing and iteration.

Permission Validation: Application-layer enforcement of tool permissions preventing bypass of human approval workflows, essential for enterprise deployment trust.

Generic UI Components: Reusable chat panel components that integrate into any internal tool with minimal engineering effort, maintaining consistent user experience.

Task Orchestration: Integration with existing workflow management systems allowing agents to process queued tasks and escalate appropriately.

Production Validation

Framework has demonstrated reliability across diverse use cases:

Mo: Personalized medical assistant requiring high accuracy and safety
Support Automation: Customer-facing automated resolution requiring tone and accuracy
Operations Agents: Internal tools requiring database modifications and external communications
Sales AI: Revenue-impacting interactions requiring business context

This diversity validates the framework's generalizability and robustness across different AI agent requirements and operational contexts.

Development Acceleration