TL;DR: AI agents fail when they can't reliably chain API calls together. The Arazzo Generator automatically discovers workflow patterns from OpenAPI specs, and the Runner executes them deterministically. This means agents that actually work in production instead of breaking on the second API call.
Why Agents Need This
Your AI agent can write perfect code, analyze complex data, and generate creative content. But ask it to book a flight, and it falls apart.
The problem isn't the reasoning. It's the execution. Real tasks require multiple API calls in sequence: authenticate, search flights, check availability, reserve seats, process payment, send confirmation.
Most agents handle this with brittle prompt engineering: "First call the auth endpoint, then use that token to call the search endpoint..." When any step fails, the whole workflow breaks. The agent retries random steps, passes parameters incorrectly, or gives up entirely.
Production systems need deterministic workflows. Not LLM guesswork about which API to call next.
The Missing Standard
OpenAPI describes individual endpoints perfectly. But it doesn't describe how endpoints connect into workflows. That gap kills agent reliability.
You get documentation like this:
# What OpenAPI gives you
POST /auth/login
GET /flights/search
POST /bookings/create
GET /bookings/{id}/confirm
POST /payments/process
But agents need this:
# What agents actually need
1. Login (POST /auth/login) -> get auth token
2. Search flights (GET /flights/search) -> get flight options
3. Create booking (POST /bookings/create) -> reserve seat
4. Process payment (POST /payments/process) -> charge card
5. Confirm booking (GET /bookings/{id}/confirm) -> final verification
The Arazzo Specification fills this gap. It is a standard from the OpenAPI Initiative that describes multi-step API workflows in machine-readable format.
How Arazzo Engine Works
Two components solve the workflow problem:
Arazzo Generator: Analyzes OpenAPI specs and automatically identifies logical workflow patterns using AI. No manual mapping required.
Arazzo Runner: Executes workflows deterministically with proper error handling, retries, and parameter passing.
Think of it as infrastructure for reliable agent execution.
See It Work
Generate workflows from any OpenAPI spec:
# Install the generator
pip install arazzo-generator
# Generate workflows from your API
arazzo-generator generate https://api.example.com/openapi.json -o workflows.yaml
The generator discovers patterns like this:
workflows:
- workflowId: book-flight
description: Complete flight booking workflow
steps:
- stepId: authenticate
operationId: login
outputs:
authToken: $response.body.token
- stepId: search-flights
operationId: searchFlights
parameters:
- name: Authorization
value: $steps.authenticate.outputs.authToken
- name: origin
value: $inputs.origin
- name: destination
value: $inputs.destination
outputs:
flightOptions: $response.body.flights
- stepId: create-booking
operationId: createBooking
parameters:
- name: Authorization
value: $steps.authenticate.outputs.authToken
- name: flightId
value: $steps.search-flights.outputs.flightOptions[0].id
outputs:
bookingId: $response.body.bookingId
- stepId: process-payment
operationId: processPayment
parameters:
- name: Authorization
value: $steps.authenticate.outputs.authToken
- name: bookingId
value: $steps.create-booking.outputs.bookingId
- name: paymentMethod
value: $inputs.paymentMethod
Now your agent can execute the entire workflow reliably:
from arazzo_runner import ArazzoRunner
runner = ArazzoRunner()
result = runner.execute(
workflow_file="workflows.yaml",
workflow_id="book-flight",
inputs={
"origin": "NYC",
"destination": "LAX",
"paymentMethod": "card_12345"
}
)
The runner handles authentication, parameter passing, error recovery, and step dependencies. Your agent focuses on business logic instead of API orchestration.
What This Means for Agents
Before Arazzo: Agents guess at workflow steps, pass parameters incorrectly, and break when APIs change.
After Arazzo: Agents execute deterministic workflows with proper error handling and recovery.
The difference is production reliability. Instead of brittle prompt chains, you get standardized workflow execution that works consistently.
Real Examples Agents Can Execute
E-commerce Purchase:
arazzo-generator generate shop-api.yaml --workflow-descriptions "complete purchase flow"
Discovers: Product search -> Add to cart -> Apply coupon -> Process payment -> Send confirmation
User Onboarding:
arazzo-generator generate user-api.yaml --workflow-descriptions "new user setup"
Finds: Create account -> Verify email -> Setup profile -> Configure preferences -> Send welcome
Data Processing:
arazzo-generator generate data-api.yaml --workflow-descriptions "ETL pipeline"
Maps: Upload file -> Validate format -> Transform data -> Store results -> Generate report
Each workflow includes proper error handling, retry logic, and parameter validation. Agents execute them reliably without custom orchestration code.
Join Hacktoberfest 2025
The Arazzo Engine is participating in Hacktoberfest this October. Over 40 beginner-friendly issues are ready for contributors:
- Workflow generation: Improve AI-powered pattern discovery
- Runner reliability: Better error handling and recovery
- Documentation: Examples and tutorials for agent integration
- Testing: Edge cases and integration coverage
All skill levels welcome. Every contribution helps make agent workflows more reliable.
Live AMA: September 18
Join the live discussion about Arazzo and agent workflows at the Ask Me Anything with Vladimír Gorej
- When? September 18
- What will we learn? How Arazzo makes agent runtimes more predictable
- Why attend? We will have live demos of workflow execution and a walkthrough for any Hacktoberfest contributors.
Vladimír is a former Principal Engineer at Oracle, GitHub Star, and co-founder of SpecLynx. He knows OpenAPI tooling and how standards actually work in production.
Why This Matters Now
Agentic systems do real work only when they can call the right tools, in the right order, with the right credentials. Prompt engineering won't scale to complex multi-API workflows.
Standards solve this, as they provide:
- Deterministic execution instead of LLM guesswork
- Proper error handling and recovery patterns
- Parameter validation and type safety
- Auditability for production systems
The Arazzo Specification gives agents the infrastructure they need to work reliably at scale.
Getting Started
Install the generator:
pip install arazzo-generator
Generate workflows from your OpenAPI specs:
# Basic generation
arazzo-generator generate api.yaml
# Focus on specific workflows
arazzo-generator generate api.yaml --workflow-descriptions "user onboarding" "checkout flow"
# Validate output
arazzo-generator validate workflows.arazzo.yaml
Integrate with your agent framework:
# Use with any agent runtime
runner = ArazzoRunner()
result = runner.execute(
workflow="user-onboarding",
inputs=agent_context
)
The workflows follow the official Arazzo specification. They work with any compliant runner and integrate with existing agent architectures.
The Infrastructure Difference
Agents are infrastructure. They need reliable tools, not creative interpretations of API documentation.
Arazzo provides the missing layer between OpenAPI specs and agent execution. It makes workflows explicit, deterministic, and auditable.
Your agents stop guessing about API sequences. They execute proven patterns with proper error handling.
No more brittle prompt chains. No more debugging why the booking failed at step 3. Just reliable workflow execution that works in production.
The Arazzo Engine is open source and part of Hacktoberfest 2025. Contribute at github.com/jentic/arazzo-engine or join the live AMA on September 18.
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