Handling Complex Logic: A Guide to Multi-Step Agentic Workflows

In modern application development, a seemingly simple request like "find a customer's order status" can quickly spiral into a complex web of interactions. You might need to query a user database for an ID, hit an orders microservice, call a third-party shipping API, and then stitch all that data together. This brittle, scattered logic is difficult to maintain, secure, and scale.

What if you could define that entire complex process once and call it with a single, simple line of code?

Welcome to the power of multi-step agentic workflows. With a platform like Searches.do, you can transform complex data operations into robust, reusable APIs. This is the core of our "Search as Software" philosophy: intelligent search, delivered as code.

Beyond Simple Lookups: The Need for Workflows

A simple search is powerful. Retrieving a user record by email is a common and essential task:

// Simple, powerful, and clean.
const customer = await searches.run('find-customer-by-email', {
  email: 'jane.doe@example.com'
});

But reality is rarely this simple. Your business logic often asks more complex questions:

• "Find the customer, but only if their subscription is active, which we need to check via the Stripe API."
• "Get the order details, then use the tracking number to fetch the live shipping status from a partner API."
• "Before creating a new user, run a sequence of checks: does the email already exist? Does it pass our fraud detection service? Is it in our CRM?"

Trying to orchestrate this logic in your frontend or a single microservice leads to tightly coupled code that's a nightmare to debug and update. This is where agentic workflows shine.

What is a Multi-Step Agentic Workflow?

A multi-step agentic workflow is a defined sequence of actions, decisions, and data retrieval steps encapsulated within a single, autonomous agent. Think of it as giving a high-level directive to an expert assistant.

You don't tell the assistant how to do every little thing; you just tell it what you want.

Task: "Get the full details and live status for order 12345."

An agent built on Searches.do would autonomously execute a workflow like this:

1. Step 1: Find Order. Executes an internal search (find-order-by-id) to retrieve the core order data from your database.
2. Step 2: Enrich with Customer Data. Uses the customer_id from the order to call another agent (find-customer-by-id) to get the customer's name and contact information.
3. Step 3: Get Live Shipping Status. Extracts the tracking_number and makes a secure HTTP request to your shipping partner's API.
4. Step 4: Assemble & Return. Combines the results from all previous steps into a single, clean, and predictable JSON object.

From your application's perspective, the complexity is gone. You still make one simple call:

// The underlying complexity is handled by the agent.
const orderDetails = await searches.run('get-full-order-details', {
  orderId: '12345'
});

This entire sequence is defined as 'Business-as-Code' within the Searches.do platform, turning your scattered logic into a centralized, managed, and scalable asset.

The Benefits of the Agentic Approach

Adopting multi-step agentic workflows for your data retrieval and business logic offers immediate, powerful advantages.

1. Radical Simplicity & Encapsulation

Your application code becomes declarative. Instead of writing how to fetch and combine data, you simply ask for what you need. The complex orchestration is entirely encapsulated within the agent.

2. Enhanced Security

Instead of exposing database credentials or a dozen different service-to-service API keys to your applications, you create a single, secure entry point. The agent acts as a hardened proxy, only exposing the exact parameters and data you need. This dramatically reduces your attack surface and abstracts away sensitive access patterns, as noted in our FAQs.

3. Unmatched Maintainability

Did your shipping partner just release v3 of their API? No problem. You update the http-request step in your get-full-order-details agent once. Every application, service, and admin tool that uses this agent is instantly updated without a single line of code being redeployed.

4. Scalability as a Service

Each agent is a stateless, scalable endpoint. As your business grows, you can confidently reuse these agentic workflow building blocks across web, mobile, and internal applications, ensuring consistency and reliability without rewriting the same logic over and over.

More Than Just Retrieval

The concept of a "search" agent extends far beyond simply finding records. It's a versatile pattern for any data-oriented task:

• Data Aggregation: Create an agent to "generate a user's monthly activity report" by pulling data from your application DB, your analytics platform, and your payment processor.
• Real-time Validation: Build a validate-new-user agent that orchestrates checks against multiple systems to approve or deny a signup in real-time.
• Automated Actions: A process-refund agent could find an order, call the Stripe API to issue the refund, update the order status in your database, and trigger an email notification—all in one atomic, auditable workflow.

Conclusion: Build Smarter with Searches.do

Simple key-value lookups are the foundation, but the future of scalable application architecture lies in composing those lookups into intelligent, multi-step workflows. By treating your complex data retrieval logic as Search as Software, you create a library of powerful, secure, and reusable business capabilities.

Stop scattering your logic. Start building intelligent agents.

Ready to turn your most complex queries into simple, powerful APIs? Explore Searches.do and build your first agent today.