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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Sequential agent chains make every agent wait for the one before it, even when the agents do not need each other’s output. The fix is event-driven concurrency: start each independent agent or task as soon as its inputs exist, record every completion or failure as a state change, and join results only where a dependent step needs them. Dependent steps stay in order. The orchestrator also needs explicit concurrency limits, timeouts, retry rules, error handling, and observability.
The pattern does not come with a universal speed-up figure. How much time you save depends on which tasks can genuinely overlap, how long each one runs, provider rate limits, aggregation cost, retries, and failures. Treat the gain as something to measure in your own workflow.
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Where the latency tax comes from
A sequential chain charges every step for the steps before it. If a sourcing agent, a pricing agent, and a policy-check agent run in turn, the elapsed time is the sum of all three, even when the pricing and policy checks only read the original request. That waiting is the latency tax: delay the problem does not require, imposed by the order in which the orchestrator happens to call things.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesIllustrative arithmetic, not a measurement: if three independent agents take 40, 25, and 30 seconds, the chain takes 95 seconds. If the three overlap and nothing else adds time, elapsed time approaches the 40 seconds of the slowest branch. Queueing, rate limits, aggregation, and retries all push the real figure up, which is why the pattern has to be measured rather than assumed.
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Temporal’s official Parallel Execution documentation puts the problem this way:
“In sequential execution, operations run one after another, causing unnecessary delays when multiple independent operations could run simultaneously.”
The passage names no individual author, so credit the statement to Temporal’s documentation.
The shape of the fix is a fan-out followed by a join:
request arrives ├─ agent A starts ── completion/failure event ──┐ ├─ agent B starts ── completion/failure event ──┼─ join/aggregate ── dependent next step └─ agent C starts ── completion/failure event ──┘
Each branch reports a completion or a failure event. The join waits according to the policy you chose, and only then does the dependent step begin.
The event-driven pattern, step by step
These six steps distill concepts that AWS Step Functions, Temporal, and OpenAI’s documentation describe. None of those sources prescribes one mandatory agent-specific recipe, so adapt the join and failure rules to your own workflow.
1. Map dependencies and find independent work
Draw each task’s inputs and outputs as a dependency graph. A task can start alongside others when all of its inputs already exist and it does not read output from a task that is still running. If two tasks write to the same shared resource, record that as a dependency too, because only one writer may update it at a time. The Realtime example later in this article shows why.
AWS Step Functions models a workflow as event-driven steps. Its flow states include conditional Choice, Wait, Map, and Parallel states, while Task states represent work performed by another service or API.
2. Dispatch tasks without waiting in series
Start each eligible task asynchronously or as a parallel branch. Give each one a complete input and an output contract, such as a fixed list of fields, so it can finish without asking the orchestrator for missing context. A task that must round-trip to the orchestrator for every detail rebuilds the serial chain in a different form.
AWS describes Parallel states as running branches simultaneously and proceeding once the branches complete. Temporal’s pattern launches independent activities or child workflows asynchronously.
3. Record each outcome as an event
Track every task in one of six states: pending, running, succeeded, failed, timed out, or cancelled. Let each completion or failure event update that workflow state. Avoid sequencing that lives only in memory inside a function call, because a crash or timeout then leaves no record of which branches finished.
4. Join only where a dependent step needs the results
Decide the join policy before you write the branches. The options include waiting for every branch, accepting a quorum (for example, two of three reviewers), tolerating failures in optional branches, or returning as soon as a required result arrives. The platforms describe joins differently. AWS’s Parallel state collects branch results into an ordered array, so downstream code can read them by position rather than by arrival order. The sources do not prescribe a universal join policy.
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5. Define what happens when a branch fails
Specify a timeout, a retry rule, cancellation behavior, what a partial result looks like, and any compensation step that undoes completed work. A branch failure must not leave the workflow waiting forever. AWS’s Parallel state manages timeouts and errors as part of the state, and Temporal’s pattern includes error handling for concurrent activities.
6. Bound concurrency and observe every task
Set concurrency limits from provider quotas, cost ceilings, and the capacity of downstream systems rather than from a platform default. Record each task’s ID, start and end times, outcome, and retry count. Those fields expose the serial bottlenecks and slow branches that a single end-to-end timer hides.
How the platforms express parallel work
The platforms below use different primitives, so compare them on what each one documents. “Not stated” means the cited documentation does not address that point.
| Platform | How parallel work is expressed | How results come back | Documented constraints or defaults |
|---|---|---|---|
| AWS Step Functions, Parallel state | Branches run simultaneously | Results collected into an ordered array; the state proceeds when the branches complete | Timeouts and errors managed by the state; AWS notes that simpler applications may be better served by simpler approaches |
| AWS Step Functions, Distributed Map state | Processes dataset items concurrently; concurrency can be specified | Not stated | Omitting the concurrency value or setting it to zero runs 10,000 parallel child workflow executions (AWS page accessed in 2026) |
| Temporal | Independent activities or child workflows launched asynchronously | Not stated in the cited pattern description | Error handling and controlled parallelism described |
| OpenAI Realtime API | Multiple simultaneous out-of-band Responses | Not stated | Only one Response can write to the default Conversation at a time |
| OpenAI Agents SDK | Not stated; the quickstart describes backend orchestration logic, including handoffs between agents | Not stated | Not stated |
In a Realtime design that fans out several Responses, treat the default Conversation as a single-writer resource. Route writes to it through one step at a time, and let the out-of-band Responses run concurrently around that step.
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Choosing an approach
Compare candidate approaches on these axes:
- dependency and branching representation;
- result joining and partial-result behavior;
- retry, timeout, cancellation, and failure semantics;
- concurrency limits and back-pressure;
- workflow state, replay or history, debugging, and visibility;
- operational burden and cost for the expected workload;
- data retention and third-party tool handling.
Managed workflow services such as AWS Step Functions declare branches, joins, and error handling as states. Code-first durable systems such as Temporal express the same ideas as ordinary code that launches activities or child workflows. Direct API coordination, such as running several simultaneous Responses through the OpenAI Realtime API, leaves more of the state and join logic in your application. The sources do not settle vendor pricing, service levels, or a complete product ranking, so check cost and support terms separately.
What the evidence does and does not show
No comparable, dated latency study or benchmark supports a universal speed-up for event-driven agent workflows. The gain is a property of your workflow: which tasks overlap, how long they run, and what your provider’s rate limits and retries add.
One platform number appears often in this area. AWS’s Distributed Map documentation says that omitting the concurrency value, or setting it to zero, runs 10,000 parallel child workflow executions. That is the default for that AWS state, as shown on a page accessed in 2026. It is not a recommendation for agent orchestration, and it is not a performance result.
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Platform details change. Some OpenAI reference material was captured months before this article was written, and the AWS figure is tied to its 2026 access date. Confirm each value on the vendor’s current page before building on it.
Data handling when agents call outside services
Concurrency multiplies the number of outbound calls, so data handling belongs in the design from the start. OpenAI’s data-controls documentation says background Responses retain response data temporarily so that clients can poll for results. It also says that data sent to remote MCP servers is subject to those servers’ retention policies. Check three things before you fan out:
Quick Recap
- Which branches send data to a model provider, and whether any of them run as background Responses.
- Which tools are remote MCP servers, and what each server’s retention policy says.
- Whether a retried or fanned-out branch would send the same sensitive input to another service that should not receive it.
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