Building Your First Agent-to-Agent Workflow

Agent-to-Agent (A2A) workflows represent the next evolution of automation. Instead of a single AI agent handling everything, multiple specialized agents collaborate to accomplish complex tasks. In this tutorial, we will walk through building your first A2A workflow from scratch.

Prerequisites

Before we begin, make sure you have:

• A SharksAPI.AI account with API access enabled
• Basic understanding of REST APIs and JSON
• Python 3.10+ or Node.js 18+ installed
• Familiarity with async programming concepts

Understanding the A2A Architecture

In an A2A workflow, agents play different roles:

• Orchestrator Agent: Coordinates the overall workflow, delegates tasks, and aggregates results.
• Specialist Agents: Handle specific domains like data retrieval, analysis, content generation, or external service integration.
• Monitor Agent: Tracks the progress of the workflow, handles timeouts, and manages error recovery.

Step 1: Define Your Agent Cards

Every agent in an A2A system publishes an Agent Card, a JSON-LD document that describes its capabilities. Here is an example:

{
  "@context": "https://schema.org/Agent",
  "name": "DataFetcherAgent",
  "description": "Retrieves and normalizes data from various APIs",
  "capabilities": [
    {
      "name": "fetch_api_data",
      "input": {"type": "object", "properties": {"url": {"type": "string"}, "method": {"type": "string"}}},
      "output": {"type": "object", "properties": {"data": {"type": "any"}, "status": {"type": "integer"}}}
    }
  ],
  "protocols": ["a2a/1.0", "mcp/1.1"],
  "authentication": ["oauth2", "api_key"]
}

Step 2: Register Your Agents

Once you have defined your agent cards, register them with the SharksAPI.AI agent registry. This makes them discoverable by other agents in your organization:

import sharksapi

client = sharksapi.Client(api_key="your-api-key")

# Register a specialist agent
agent = client.agents.register(
    name="DataFetcherAgent",
    card=agent_card,
    endpoint="https://your-service.com/agent",
    visibility="organization"  # or "public"
)

Step 3: Build the Orchestrator

The orchestrator is the brain of your A2A workflow. It decides which agents to call, in what order, and how to handle the results:

async def orchestrate_workflow(task):
    # Step 1: Discover available agents
    agents = await client.agents.discover(
        capabilities=["fetch_api_data", "analyze_data", "generate_report"]
    )

    # Step 2: Delegate data fetching
    raw_data = await agents["DataFetcher"].execute(
        task="fetch_api_data",
        params={"url": task.data_source, "method": "GET"}
    )

    # Step 3: Delegate analysis
    analysis = await agents["DataAnalyzer"].execute(
        task="analyze_data",
        params={"data": raw_data, "analysis_type": "trends"}
    )

    # Step 4: Generate final report
    report = await agents["ReportGenerator"].execute(
        task="generate_report",
        params={"analysis": analysis, "format": "html"}
    )

    return report

Step 4: Handle Errors and Fallbacks

Robust A2A workflows need error handling at every step. The A2A protocol supports several patterns:

• Retry with backoff: Automatically retry failed agent calls with exponential backoff.
• Fallback agents: Define alternative agents that can handle the same capability if the primary agent fails.
• Partial results: Accept partial results when some agents fail and still produce useful output.
• Timeout management: Set per-agent and per-workflow timeouts to prevent hanging workflows.

Step 5: Monitor and Iterate

Once your workflow is running, use the SharksAPI.AI dashboard to monitor agent performance, track success rates, identify bottlenecks, and optimize. Key metrics to watch:

• Agent response time: How quickly each agent completes its tasks.
• Success rate: Percentage of tasks completed without errors.
• Token usage: LLM token consumption per workflow execution.
• Cost per workflow: Total cost including compute, API calls, and LLM usage.

Best Practices

After building dozens of A2A workflows, here are our top recommendations:

1. Keep agents focused: Each agent should do one thing well rather than being a generalist.
2. Use structured outputs: Define clear JSON schemas for agent inputs and outputs to minimize misunderstandings.
3. Implement circuit breakers: Prevent cascading failures when one agent in the chain goes down.
4. Version your agent cards: As capabilities evolve, maintain backward compatibility through versioning.
5. Test with mock agents: Build test doubles for your agents to enable reliable integration testing.

With these foundations in place, you are ready to build increasingly sophisticated A2A workflows that can handle real-world business automation challenges.