As AI agents take on complex enterprise tasks, the ability to coordinate multiple actions into cohesive workflows is critical. The Agentic API framework, introduces chaining as a powerful feature to enable AI agents to execute multi-step processes efficiently. Unlike traditional APIs that focus on isolated data operations, chaining empowers agents to orchestrate tasks like booking a meeting, notifying attendees, and updating calendars in a single, streamlined flow. This blog post explores how chaining transforms agent-driven automation, making it a cornerstone of scalable, intelligent systems.
The Need for Workflow Orchestration
Conventional APIs, rooted in CRUD (Create, Read, Update, Delete) and REST, are designed for single, resource-focused operations, such as POST /users or GET /orders. However, real-world tasks often require multiple steps. For example, scheduling a meeting involves checking availability, reserving a slot, and sending confirmations—each potentially requiring separate API calls. Traditional APIs force agents to manage this coordination externally, leading to brittle logic, increased latency, and higher error rates. Protocols like MCP attempt to address this through agent-to-agent communication, but they introduce complexity without solving the core issue: APIs that lack native workflow support.
Agentic API’s chaining feature, part of the Orchestrate category in the ACTION taxonomy, embeds workflow coordination directly into the API layer. By supporting methods like CHAIN, SCHEDULE, and RETRY, it allows agents to define and execute sequential or conditional tasks, aligning with the Agent Experience (AX) demand for intuitive, reliable automation.
How Chaining Works
Chaining enables agents to link actions into a single request, with each step’s output feeding into the next. The CHAIN /request endpoint, detailed in the white paper, accepts a payload specifying a sequence of tasks, each with a verb, path, and parameters. For instance:
{
"chain": [
{
"method": "SEARCH",
"path": "/orders",
"params": {"query": "pending"}
},
{
"method": "NOTIFY",
"path": "/notify",
"params": {"recipient": "user@example.com", "message": "Order processed"}
}
]
}The API processes each step, returning a response with results or errors for traceability:
{
"results": [
{"step": {"method": "SEARCH", "path": "/orders"}, "result": {"status": "completed"}},
{"step": {"method": "NOTIFY", "path": "/notify"}, "result": {"status": "completed"}}
]
}Chaining supports features like retry policies, status metadata, and fallback actions (e.g., RETRY /action or ESCALATE /workflow), ensuring robustness. OpenAPI extensions like x-workflow document dependencies, enabling agents to adapt dynamically to failures or context changes.
Why Chaining Matters
Chaining reduces the cognitive and operational burden on agents by embedding orchestration logic in the API. This minimizes latency, as tasks are executed server-side, and enhances reliability through standardized error handling. In finance, an agent can chain VALIDATE /transaction and TRANSFER /funds to ensure compliance before execution. In healthcare, chaining TRIAGE /patient and NOTIFY /doctor streamlines emergency workflows.
The white paper’s proof-of-concept demonstrates chaining’s impact: a chained booking task reduced execution time and errors compared to CRUD-based APIs. Unlike MCP’s protocol-heavy approach, chaining leverages REST infrastructure, ensuring compatibility and scalability.
The Future of Agentic Workflows
Chaining positions Agentic API as a foundation for adaptive automation. It enables action registries to catalog workflows and supports domain-specific methods like SETTLE (finance) or REFER (healthcare). By making orchestration intuitive, Agentic API empowers organizations to build resilient systems that scale. APIs must “enable execution”—chaining delivers on that promise, turning APIs into dynamic workflow engines.