Introduction
Integrating different Large Language Models (LLMs) into agentic workflows involves creating systems where autonomous agents utilize various LLMs to perform tasks, make decisions, and interact with each other or users. Standardizing these workflows is challenging due to differences in LLM capabilities, APIs, data formats, and interaction protocols.
Challenges in Standardization
Diverse LLM APIs: Each LLM may have its own API specifications, authentication methods, and rate limits. Inconsistent Data Formats: Varying input/output formats and tokenization methods across LLMs complicate integration. Variable Capabilities: LLMs differ in language understanding, reasoning abilities, and supported features. Scalability and Performance: Orchestrating multiple LLMs can introduce latency and resource management issues. Security and Compliance: Ensuring data privacy and compliance across different LLMs is critical.
Specification for Integrating Different LLMs in Agentic Workflows
To address these challenges, the following specification aims to standardize the integration of different LLMs within agentic workflows:
- Unified API Interface: Abstract the differences between LLM APIs by defining a common interface.
- Standardized Data Formats: Use consistent data schemas for inputs and outputs, typically in JSON.
- Agent Definition: Describe agents with their roles, capabilities, and associated LLMs.
- Workflow Definition: Define the sequence, conditions, and logic of agent interactions and data flow.
- LLM Metadata: Include details about each LLM, such as model name, version, capabilities, and parameters.
- Error Handling and Fallbacks: Standardize error responses and define fallback mechanisms.
- Security and Compliance: Incorporate authentication, authorization, and data handling policies.
Implementation Using JSON Descriptor
Below is a JSON schema implementing the above specification:
{
"$schema": "http://json-schema.org/draft-07/schema#",
"title": "LLMAgenticWorkflow",
"type": "object",
"properties": {
"workflowId": { "type": "string" },
"agents": {
"type": "array",
"items": { "$ref": "#/definitions/agent" }
},
"llms": {
"type": "array",
"items": { "$ref": "#/definitions/llm" }
},
"workflowSequence": {
"type": "array",
"items": { "$ref": "#/definitions/workflowStep" }
},
"dataFormats": {
"type": "object",
"properties": {
"inputFormat": { "type": "string" },
"outputFormat": { "type": "string" }
},
"required": ["inputFormat", "outputFormat"]
},
"errorHandling": { "$ref": "#/definitions/errorHandling" },
"security": { "$ref": "#/definitions/security" }
},
"required": ["workflowId", "agents", "llms", "workflowSequence", "dataFormats"],
"definitions": {
"agent": {
"type": "object",
"properties": {
"agentId": { "type": "string" },
"role": { "type": "string" },
"capabilities": { "type": "array", "items": { "type": "string" } },
"llmId": { "type": "string", "description": "Reference to an LLM used by this agent." },
"parameters": {
"type": "object",
"description": "Specific parameters for the LLM (e.g., temperature, max_tokens)."
}
},
"required": ["agentId", "role", "llmId"]
},
"llm": {
"type": "object",
"properties": {
"llmId": { "type": "string" },
"modelName": { "type": "string" },
"version": { "type": "string" },
"apiEndpoint": { "type": "string" },
"apiKey": { "type": "string" },
"inputFormat": { "type": "string" },
"outputFormat": { "type": "string" },
"capabilities": { "type": "array", "items": { "type": "string" } }
},
"required": ["llmId", "modelName", "apiEndpoint", "apiKey"]
},
"workflowStep": {
"type": "object",
"properties": {
"stepId": { "type": "string" },
"agentId": { "type": "string" },
"input": { "type": "string" },
"output": { "type": "string" },
"conditions": { "type": "string" }
},
"required": ["stepId", "agentId"]
},
"errorHandling": {
"type": "object",
"properties": {
"errorCodes": {
"type": "array",
"items": { "$ref": "#/definitions/errorCode" }
},
"fallbackAgentId": { "type": "string" }
},
"required": ["errorCodes"]
},
"errorCode": {
"type": "object",
"properties": {
"code": { "type": "string" },
"description": { "type": "string" },
"action": { "type": "string" }
},
"required": ["code", "description", "action"]
},
"security": {
"type": "object",
"properties": {
"authentication": { "type": "string" },
"authorization": { "type": "string" },
"encryption": { "type": "string" },
"compliance": { "type": "string" }
},
"required": ["authentication", "authorization"]
}
}
}
Explanation of Key Components
- Agents: Each agent represents an autonomous entity in the workflow that utilizes an LLM.
- LLMs: Defines the different LLMs integrated into the workflow with specific details.
- WorkflowSequence: Specifies the steps in the workflow, indicating which agent acts at each step.
- DataFormats: Standardizes the input and output data formats for consistency.
- ErrorHandling: Defines error management, including error codes and fallback mechanisms.
- Security: Details authentication, authorization, and compliance requirements.
Example of an LLM Agentic Workflow Descriptor
{
"workflowId": "workflow-001",
"agents": [
{
"agentId": "agent-qa",
"role": "QuestionAnswering",
"capabilities": ["text-comprehension"],
"llmId": "llm-gpt4",
"parameters": {
"temperature": 0.5,
"max_tokens": 150
}
},
{
"agentId": "agent-sentiment",
"role": "SentimentAnalysis",
"capabilities": ["sentiment-analysis"],
"llmId": "llm-bert",
"parameters": {}
},
{
"agentId": "agent-fallback",
"role": "FallbackQA",
"capabilities": ["text-comprehension"],
"llmId": "llm-roberta",
"parameters": {
"temperature": 0.5,
"max_tokens": 150
}
}
],
"llms": [
{
"llmId": "llm-gpt4",
"modelName": "GPT-4",
"version": "4.0",
"apiEndpoint": "https://api.openai.com/v1/chat/completions",
"apiKey": "OPENAI_API_KEY",
"inputFormat": "application/json",
"outputFormat": "application/json",
"capabilities": ["text-generation", "text-comprehension"]
},
{
"llmId": "llm-bert",
"modelName": "BERT",
"version": "1.0",
"apiEndpoint": "https://api.example.com/bert/analyze",
"apiKey": "BERT_API_KEY",
"inputFormat": "text/plain",
"outputFormat": "application/json",
"capabilities": ["sentiment-analysis"]
},
{
"llmId": "llm-roberta",
"modelName": "RoBERTa",
"version": "1.0",
"apiEndpoint": "https://api.example.com/roberta/completions",
"apiKey": "ROBERTA_API_KEY",
"inputFormat": "application/json",
"outputFormat": "application/json",
"capabilities": ["text-generation", "text-comprehension"]
}
],
"workflowSequence": [
{
"stepId": "step-1",
"agentId": "agent-qa",
"input": "userQuestion",
"output": "answer",
"conditions": "Start"
},
{
"stepId": "step-2",
"agentId": "agent-sentiment",
"input": "userQuestion",
"output": "sentimentScore",
"conditions": "After step-1"
}
],
"dataFormats": {
"inputFormat": "application/json",
"outputFormat": "application/json"
},
"errorHandling": {
"errorCodes": [
{
"code": "LLM_TIMEOUT",
"description": "The LLM did not respond in time",
"action": "Retry"
},
{
"code": "LLM_ERROR",
"description": "Error returned from LLM",
"action": "Use fallback agent"
}
],
"fallbackAgentId": "agent-fallback"
},
"security": {
"authentication": "API Key",
"authorization": "Role-Based Access Control",
"encryption": "TLS1.2",
"compliance": "GDPR"
}
}
Workflow Explanation
Step 1: agent-qa uses llm-gpt4 to generate an answer to userQuestion. Step 2: agent-sentiment uses llm-bert to analyze the sentiment of userQuestion. Error Handling: If llm-gpt4 fails, agent-fallback with llm-roberta provides the answer.
Conclusion
This specification provides a structured approach to integrating different LLMs into agentic workflows using JSON descriptors. By standardizing agent definitions, LLM metadata, data formats, and error handling, we enhance interoperability and create robust systems capable of leveraging the strengths of various LLMs. Implementing security measures ensures compliance and data protection across the workflow.