AI API Gateway for Agentic Workflows

Build autonomous AI systems with intelligent workflow orchestration. Coordinate multiple agents, manage complex task chains, and enable self-directing AI workflows through unified gateway APIs.

Explore Patterns View Code
Multi-Agent Workflow
🎯

Orchestrator Agent

Plans and coordinates tasks

→
🤖

Research Agent

Gathers information

🤖

Analysis Agent

Processes data

→
⚙️

Tool Execution

API calls and actions

Agentic Features

Core capabilities for autonomous AI systems.

🔄

Workflow Orchestration

Define complex multi-step workflows with conditional branching, parallel execution, and dynamic task allocation across AI agents.

🤝

Multi-Agent Coordination

Enable multiple AI agents to collaborate on tasks with shared context, message passing, and role-based responsibilities.

🛠️

Tool Integration

Connect agents to external APIs, databases, and services through unified tool interfaces with automatic retry and error handling.

🧠

State Management

Persist and share state across workflow steps. Enable agents to access conversation history and task context.

⚡

Dynamic Routing

Route tasks to appropriate agents based on capabilities, load, or context. Enable automatic failover and load balancing.

📊

Execution Monitoring

Track workflow progress, agent decisions, and tool usage. Debug complex multi-agent interactions with detailed logs.

Workflow Patterns

Proven patterns for agentic AI systems.

Sequential

Chain of Agents

Execute agents in sequence, passing output from one as input to the next.

  • Linear task decomposition
  • Output transformation pipeline
  • Progressive refinement
  • Error propagation handling
Parallel

Fan-Out/Fan-In

Run multiple agents in parallel and aggregate their results.

  • Parallel research tasks
  • Result aggregation
  • Timeout handling
  • Partial result acceptance
Iterative

Refinement Loop

Iteratively improve results through multiple agent passes.

  • Quality threshold checking
  • Self-critique and revision
  • Maximum iteration limits
  • Convergence detection
Hierarchical

Manager-Worker

Manager agent delegates tasks to specialized worker agents.

  • Task decomposition
  • Capability matching
  • Result synthesis
  • Priority management

Implementation Guide

Build agentic workflow systems.

agentic_gateway.py Python 
class AgenticGateway:
    """Gateway for agentic AI workflows"""
    
    def __init__(self, agent_registry, tool_registry):
        self.agents = agent_registry
        self.tools = tool_registry
        self.state_store = StateStore()
        
    async def execute_workflow(
        self,
        workflow: Workflow,
        context: dict
    ) -> WorkflowResult:
        """Execute a multi-agent workflow"""
        
        # Initialize workflow state
        state = WorkflowState(
            workflow_id=workflow.id,
            context=context,
            step_results={}
        )
        
        for step in workflow.steps:
            try:
                # Get agent for step
                agent = self.agents.get(step.agent_id)
                
                # Build agent input
                agent_input = self.build_input(
                    step, state, context
                )
                
                # Execute agent with tools
                result = await agent.execute(
                    input=agent_input,
                    tools=self.get_tools(step.allowed_tools),
                    state=state
                )
                
                # Store result
                state.step_results[step.id] = result
                
                # Check for workflow modification
                if result.branch:
                    workflow = self.apply_branch(workflow, result.branch)
                
                # Check for early termination
                if result.terminate:
                    break
                    
            except AgentError as e:
                # Handle failure
                if step.retry_policy:
                    result = await self.retry_step(step, state, e)
                else:
                    raise
        
        return WorkflowResult(
            workflow_id=workflow.id,
            final_state=state,
            success=True
        )

class Agent:
    """Individual AI agent with tool access"""
    
    def __init__(self, model, system_prompt):
        self.model = model
        self.system_prompt = system_prompt
        
    async def execute(
        self,
        input: dict,
        tools: List[Tool],
        state: WorkflowState
    ) -> AgentResult:
        """Execute agent with available tools"""
        
        messages = [
            {"role": "system", "content": self.system_prompt},
            {"role": "user", "content": input["prompt"]}
        ]
        
        while True:
            # Call LLM
            response = await self.model.chat(
                messages=messages,
                tools=self.format_tools(tools)
            )
            
            # Check if agent wants to use tools
            if response.tool_calls:
                for tool_call in response.tool_calls:
                    # Execute tool
                    tool = self.get_tool(tool_call.name, tools)
                    result = await tool.execute(
                        tool_call.arguments
                    )
                    
                    # Add tool result to messages
                    messages.append({
                        "role": "assistant",
                        "tool_calls": [tool_call]
                    })
                    messages.append({
                        "role": "tool",
                        "content": result
                    })
            else:
                # Agent finished
                return AgentResult(
                    output=response.content,
                    branch=response.metadata.get("branch"),
                    terminate=response.metadata.get("terminate")
                )

class OrchestratorAgent(Agent):
    """Agent that coordinates other agents"""
    
    async def decompose_task(
        self,
        task: str
    ) -> List[SubTask]:
        """Break down complex task into subtasks"""
        
        decomposition = await self.model.chat(
            messages=[{
                "role": "user",
                "content": f"Decompose task: {task}"
            }],
            response_format={"type": "json"}
        )
        
        return [SubTask(**t) for t in json.loads(decomposition.content)]

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