Your team runs a 12-step process. Each step depends on the one before it. Someone skips step 4, and nobody catches it until step 11 fails catastrophically.
Three hours of work undone. A frustrated team member. A deadline missed. All because there was no way to enforce that step 4 had to complete before step 5 could start.
Sequential chaining is not about making things slower. It is about making sure step 2 cannot run until step 1 actually finished correctly.
Complex work requires dependencies. Sequential chaining makes dependencies explicit and enforced.
Sequential chaining is part of the Process Control category in Layer 4. This layer handles how multiple components work together, managing the flow of data and control between different parts of your system.
Why Layer 4? The lower layers handle individual capabilities: storing data, understanding content, parsing outputs. Sequential chaining connects those capabilities into workflows where the output of one becomes the input of another.
Sequential chaining connects multiple operations where each one depends on the previous result. The output of step 1 becomes the input of step 2. If step 1 fails or produces invalid output, step 2 never runs.
This is not about running things slowly. It is about running things correctly. Each step validates its input, does its work, and passes a clean result forward. The chain only continues if every link holds.
The power is not in the sequence. It is in the guarantee that nothing downstream happens until everything upstream is verified.
Sequential chaining is not specific to AI or automation. It is the fundamental pattern for any process where order matters and dependencies exist.
Whenever step B requires the output of step A, you need a mechanism that prevents B from starting until A has successfully completed. This is the core of sequential chaining.
Click Run Chain to see how each step waits for the previous one to complete. If a step fails, the chain stops immediately.
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Click "Run Chain" to see output...
Chain functions where each returns what the next needs
Each step is a function that takes input and returns output. You compose them so the return value of one becomes the argument of the next. Clean, testable, and easy to understand.
Use queues to decouple and persist between steps
Each step reads from a queue, processes, and writes to the next queue. If a step fails, the message stays in the queue for retry. You get durability, but more infrastructure.
Use dedicated orchestration tools for complex chains
Tools like Temporal, Step Functions, or Airflow manage the chain for you. They handle retries, timeouts, and state. You define the steps and dependencies, the engine runs them.
Sequential chaining sits at the orchestration layer, taking outputs from earlier steps and feeding them to later ones. It depends on proper input/output handling and enables more complex orchestration patterns.
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Step 1 runs. You move to step 2. But step 1 actually failed silently or returned partial data. Step 2 runs on garbage, produces garbage, and you only discover it at step 7.
Instead: Every step must validate its input before processing and validate its output before passing forward. Failed validation stops the chain immediately.
Step 4 of 8 fails. You fix the issue. But now you have to start from step 1 again because there is no record of what completed and what the intermediate results were.
Instead: Persist state after each successful step. When resuming, check what already succeeded and continue from there instead of restarting.
Someone submits a request. Nothing happens for 20 minutes. They submit again. Now you have duplicate requests in different stages. Nobody knows what is happening.
Instead: Expose the chain status: which step is running, what completed, what is waiting. Give users visibility so they know their request is progressing.
You have learned how to connect steps so each depends on the previous. The natural next step is understanding how to run steps in parallel when they do not depend on each other.