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Reliable AI-agent workflows do not retry every error. They classify the failure, check whether replay is safe, apply bounded exponential backoff with jitter, enforce time limits at several layers, and persist completed stages so recovery does not repeat side effects. A client disconnect is treated separately from a server run that has actually terminated: an active background run can often be polled or reconnected, while a hard context failure requires a new session. The design below gives you a practical recovery flow for model calls, tools, handoffs, and long-running work.
How should I classify an AI-agent failure before retrying?
Classify the failure first, then choose retry, correction, compensation, resume, fallback, or human escalation. A retry is appropriate only when the underlying condition is likely to clear and replay will not duplicate an external effect.
Capture enough context to make the decision
Record the provider, model, operation, attempt number, HTTP status and provider error code, timeout layer, elapsed time, whether output streaming started, whether a tool was invoked, whether a local or external side effect may have occurred, and a correlation or trace ID. OpenAI distinguishes request errors from asynchronous turn, session, and environment failures, so inspect the event or object that actually failed instead of applying one generic handler.
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| Failure class | Recovery action | Reason |
|---|---|---|
| Rate limit, overload, temporary 5xx, network fault, or timeout | Retry within a limit using backoff and jitter; honor Retry-After when supplied |
These conditions are commonly transient. |
| Invalid request, malformed tool arguments, or context that is too large | Correct the input, reduce context, or change configuration before resubmitting | Repeating the same request repeats the cause. |
| Authentication, authorization, billing, or exhausted credits | Fix credentials, permissions, or account limits, then resubmit intentionally | The failure persists until configuration or entitlement changes. |
| Stream started or a non-idempotent side effect may have occurred | Inspect saved state and external effects; compensate or resume rather than blindly replaying | A second execution can duplicate work or mutate state twice. |
| Agent loop exceeds its turn or workflow budget | Stop the run, persist state, refine the plan, use a fallback, or escalate | Unbounded loops consume time, money, and external resources. |
| Client disconnect while the server run remains active | Poll or reconnect with the interaction or session ID | A client timeout does not prove that server-side work stopped. |
| Hard failure that terminates the execution context | Start a new session with a refined request and preserved context | The terminated context cannot be resumed. |
As concrete examples, transient candidates commonly include HTTP 408, 409, 429, 500, 502, 503, and 504, plus network and timeout errors. Google advises retrying transient 429 and 5xx responses but not retrying 400, 402, or 403 responses until the cause is corrected. A provider can still veto a replay, so status-code classification is necessary but not sufficient.
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Which timeout should I set for an AI-agent workflow?
Use separate limits for one model-call attempt, each tool call, the agent turn, and the complete workflow. The model-call timeout protects one request; it does not cap tool execution, retry backoff, handoffs, or the total run.
Model-call attempt timeout
Set an explicit deadline around every model request. When it expires, cancel that attempt, record the timeout layer, and let the retry policy decide whether a safe replay is possible. The Agents SDK describes this as a per-attempt model timeout, not an end-to-end run limit.
Tool-call timeout
Wrap every function, HTTP request, database operation, and connector in its own timeout and cancellation path. A model can return quickly while a tool hangs, so a model timeout alone cannot prevent a stuck workflow. The tool wrapper should report whether it started, completed, or may have changed external state before cancellation.
Turn and workflow deadlines
Set a larger deadline for an agent turn and a still broader deadline for the complete workflow. Both limits should stop new work, persist the latest state, and emit a terminal reason. AWS recommends controls that detect and terminate unexpected long-running agentic workflows because an agent can continue calling external systems during an unforeseen loop.
Asynchronous execution for long jobs
Research, multi-step automation, and human approval often exceed the lifetime of an ordinary HTTP connection. The Gemini API documents a pattern in which a normal connection can close after about 60 seconds; use background execution, status polling, or event replay for work that may outlast that connection. Treat the connection lifetime and the server-run lifetime as different things.
How do I design a bounded retry policy?
A safe policy combines provider advice, failure classification, replay safety, exponential backoff with jitter, an attempt limit, an overall deadline, and a shared retry budget. Never let each worker retry independently without a common cap.
- Read provider guidance. If a response supplies
Retry-After, wait at least that long. Honor an explicit provider overload or retry recommendation unless replay-safety checks reject the operation. - Classify the error. Mark transient transport, rate-limit, overload, temporary service, and timeout failures as candidates. Mark malformed input, invalid parameters, authentication, authorization, billing, exhausted credits, missing resources, and policy or safety blocks as non-retryable until corrected.
- Check the deadline and budget. Stop when the workflow deadline, maximum-attempt setting, or shared retry budget is exhausted. A budget shared across workers or tenants prevents an upstream outage from becoming a retry storm.
- Calculate backoff. Increase the delay after each failed attempt and add random jitter so many agents do not wake and retry simultaneously. Cap the delay so one attempt cannot consume the entire workflow deadline.
- Re-evaluate on every attempt. A response that was safe to retry before streaming began may become unsafe after a response starts or a tool executes. Reclassify the current attempt rather than blindly following the original decision.
- Persist the decision. Log the selected action, delay, remaining budget, and reason so operators can explain why a request was retried, stopped, or escalated.
Illustrative policy pseudocode
on_failure(error, context):
record(provider, model, operation, attempt, status, timeout_layer,
elapsed, response_started, side_effect_possible, trace_id)
if context.deadline_expired or context.retry_budget_empty:
persist_state_and_stop("budget_exhausted")
if error.is_invalid_input or error.is_auth or error.is_forbidden
or error.is_billing or error.is_policy_block:
correct_or_escalate(error)
if context.response_started or context.side_effect_possible:
inspect_state_and_external_effects()
if not context.replay_safe:
resume_or_compensate()
if not error.is_transient:
use_fallback_or_escalate(error)
delay = provider_retry_after or exponential_backoff_with_jitter(attempt)
wait_until(delay, but_not_past=context.deadline)
retry()The code is a control-flow example, not a universal delay schedule. Choose limits from the latency and cost budget of your own workflow, and keep them configurable by operation and provider.
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When is replaying an AI-agent request unsafe?
Replay is unsafe whenever the first attempt may already have produced output or changed state and you cannot prove that running it again is idempotent. Inspect the response and execution state before issuing a second request.
Signals that require extra care
- The response stream has started, even if the final message was never received.
- A tool may have sent an email, charged a card, created a record, deleted data, deployed code, or otherwise changed an external system.
- The request carries conversation state, a previous-response reference, or another stateful context whose replay semantics differ from a new request.
- The operation is non-idempotent or lacks an idempotency key and durable result record.
- A local side effect, such as writing a file or queue message, occurred before the network failure was reported.
The Agents SDK exposes response-started, replay-safety, stateful-request, and local-side-effect signals to a retry policy, and blocks many unsafe replays by default. Use those signals as policy inputs rather than treating every network error as proof that nothing happened.
Resume, compensate, or replay
Replay only when the operation is idempotent or the provider and your application can deduplicate it. Resume from a persisted checkpoint when the side effect already succeeded but the acknowledgment was lost. If the effect may have partially completed, query the external system, reconcile the result, and run a compensating action where one exists.
How can I resume a failed multi-step workflow without repeating work?
Split the agent plan into recoverable stages, persist each successful stage, validate its output, and restart only the failed stage. This turns a long run into a sequence of explicit recovery points.
Define stage boundaries
Make each stage have a clear input, output, timeout, owner, and completion condition. Typical boundaries are planning, retrieval, tool execution, transformation, approval, and publication. A stage should either produce a durable result or leave an unambiguous pending state.
Persist a stage record
- Workflow and stage identifiers.
- Input hash or version so a resumed stage uses the same source.
- Attempt number, start and end times, and timeout layer.
- Validated output location and schema version.
- External operation identifiers and idempotency information.
- Trace or correlation ID and the next permitted transition.
Validate before advancing
Check structure, required fields, tool-result status, authorization, and business invariants before marking a stage complete. Do not let a partially streamed answer or an unverified tool response become the input to the next stage. If validation fails, correct the input or route the stage to a fallback instead of retrying the same invalid output.
Recover only the affected stage
On a transient model failure, retry the current stage if replay is safe. If a prior tool already changed state, reconcile that effect and continue from the recorded checkpoint. Persistent failures should select a fallback path, while unrecoverable cases should preserve the state and request human intervention rather than restarting the entire plan.
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What should happen when a client disconnects while an agent is working?
First determine whether the server run is still active. A dropped socket or client-side timeout is a soft failure; a platform event that terminates the turn or session is a hard failure.
Soft timeout: reconnect or poll
- Keep the interaction, turn, or session ID before starting the request.
- After the client disconnects, query run status or reconnect to the event stream using that ID.
- Replay missed events from the provider if supported, then reconcile the final state with your persisted stage record.
- Do not submit a duplicate request merely because the original connection closed.
Background-capable interactions are especially useful for research and multi-step jobs. They let the client leave and return without assuming that server execution stopped.
Hard context failure: start a refined session
If the platform reports that the turn or context terminated, stop polling the old execution. Create a new session, preserve the validated stage outputs and relevant trace identifiers, and refine the request to address the failure. The new session is a continuation by application state, not a replay of the dead context.
How do I stop an agent loop from running forever?
Enforce limits at the turn, tool, retry, and workflow levels, and make every limit produce a recoverable terminal state. A loop should stop when it exceeds its budget even if the model keeps requesting another action.
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- Retry budget: cap retries across all workers or tenants, not just within one process.
- Time budget: enforce per-attempt, per-tool, turn, and end-to-end deadlines.
- Progress check: detect repeated tool calls, unchanged inputs, or a stage that never reaches validation.
- Resource controls: terminate unexpected long-running workflows and release tool resources when cancellation occurs.
When a budget is reached, persist the current plan and evidence, then choose a fallback or human escalation. Logging only a final timeout hides the loop that consumed the budget.
How should tools handle partial failure and side effects?
Tool wrappers need explicit lifecycle states: not started, started, succeeded, failed, canceled, and outcome unknown. The unknown state is essential when the network fails after the tool may have completed.
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Before invoking a tool
Validate arguments, authorization, and required context. Generate or propagate a correlation ID and, where the target system supports it, an idempotency key. Persist the intended operation before sending it so recovery can distinguish an unattempted call from an interrupted one.
After an ambiguous result
Query the target system by operation ID or idempotency key before retrying. If the effect exists, record the result and advance the stage. If it does not exist and replay is safe, retry within the remaining deadline. If neither outcome can be proven, pause for reconciliation or human review instead of issuing repeated writes.
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Cancellation should propagate from the workflow to the model call and every tool. For effects that cannot be rolled back, define a compensating action or mark the workflow for manual reconciliation. A timeout is not a license to assume that cancellation succeeded.
How do I observe retries, timeouts, and failures?
Instrument recovery as a first-class path, not as an exception-only log. Emit traces and metrics for every model call, tool call, handoff, retry decision, backoff delay, timeout, fallback, and human escalation.
Minimum event fields
- Trace, workflow, stage, interaction, and session IDs.
- Provider, model, operation, region or deployment when relevant, and attempt number.
- Status code, provider error code, transient-versus-persistent classification, and provider retry advice.
- Timeout layer, configured deadline, elapsed time, and cancellation result.
- Whether streaming started, replay was considered safe, and a side effect was possible.
- Backoff delay, remaining retry budget, selected fallback, and escalation reason.
Correlate the whole path
Connect model, tool, handoff, queue, and storage spans with one trace. This lets an operator identify the failing stage and see whether policy retried, resumed, compensated, or escalated. End-to-end distributed tracing is particularly important when the client has disconnected and the server continues in the background.
No official source in the material provides a benchmark percentage for AI-agent retry, timeout, or failure rates. Use your own traces to establish baselines rather than presenting an unsupported industry failure rate.
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Use a fallback when the primary provider, model, tool, or path remains unavailable after safe retries, and use a human when the result is ambiguous, high-impact, or impossible to validate automatically.
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Fallback options
- Switch to an approved model or provider for a read-only or lower-risk stage.
- Serve a previously validated result when freshness requirements allow it.
- Defer the stage to a queue and notify the caller that processing continues asynchronously.
- Reduce context or simplify the plan after a context-size or complexity failure.
Fallbacks must preserve the same replay and authorization checks as the primary path. Do not silently switch providers for an operation whose data, policy, or tool permissions differ.
Human escalation triggers
- An external side effect may have occurred but cannot be reconciled.
- The workflow reaches its deadline or retry budget without a valid output.
- Authentication, authorization, billing, or policy blocks require a person with access to correct them.
- Validation detects conflicting results or a request with material consequences.
Give the reviewer the persisted stage outputs, tool operation IDs, trace, attempted actions, and the exact decision that was blocked. A human should not have to reconstruct the run from uncorrelated logs.
Should I use durable execution for long-running agents?
For workflows that must survive process restarts, human approval, handoffs, or long waits, durable execution can store progress and resume from checkpoints instead of relying on one process and one HTTP connection. The OpenAI Agents SDK running guide names Dapr, Temporal, and Restate as integrations for durable, long-running agents, including session management and recovery across restarts.
Choose an orchestrator that can persist stage state, resume timers and approvals, propagate cancellation, and expose trace context. Durable execution does not remove the need for idempotency, timeout layers, or failure classification; it gives those policies a place to run after a worker or connection disappears.
What is a practical implementation sequence?
Implement reliability in this order so each control supports the next one.
- Define the state machine. List stages, valid transitions, terminal states, and which outputs are safe to reuse.
- Add structured failure events. Capture provider, status, timeout layer, attempt, side-effect, and trace fields on every failure.
- Separate timeout layers. Add model, tool, turn, and workflow deadlines with cancellation handling.
- Implement classification. Distinguish transient, persistent, ambiguous-side-effect, budget, disconnect, and hard-context failures.
- Add bounded retries. Use exponential backoff, jitter,
Retry-After, maximum attempts, a deadline, and a shared budget. - Gate replay. Consult response-started, replay-safety, stateful-request, and side-effect signals before resubmitting.
- Persist and validate stages. Commit outputs only after schema and business checks pass.
- Support reconnect and background status. Keep interaction IDs and reconcile missed events after client disconnects.
- Add fallbacks and escalation. Define what happens when retries cannot produce a trustworthy result.
- Test fault paths deliberately. Exercise 429, 5xx, network loss, timeout during streaming, tool completion with lost acknowledgment, client disconnect, exhausted budget, and hard context termination.
What should operators check during an incident?
- Is the failure transient, persistent, ambiguous, or caused by an exceeded budget?
- Did a response stream or tool side effect begin?
- Which timeout layer fired, and is the server run still active?
- What does the provider’s
Retry-Afteror overload guidance say? - How much retry budget and workflow deadline remain?
- What stage was last persisted and validated?
- Can the external system confirm whether the operation completed?
- Should the run resume, compensate, fall back, or escalate?
- Are traces, logs, and metrics correlated under one workflow identifier?
Frequently Asked Questions
Are retries enabled automatically in the OpenAI Agents SDK?
No. The SDK describes retries as runtime-only and opt in. Your policy must explicitly decide when to retry and can inspect attempt count, status, retry-after guidance, network and timeout classification, response-started state, replay safety, and stateful-request status.
How should I handle a streamed answer that ends halfway through?
Treat the partial stream as output that may already exist, not as proof of a clean failure. Store the partial data separately, verify whether any tool call or external effect began, and resume or reconcile from the last validated stage instead of blindly requesting the same turn again.
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What is the difference between a client timeout and a terminated agent run?
A client timeout only says that the caller stopped waiting; the server may still be processing the interaction. A terminated run is reported by the platform as a hard failure and requires a new session built from preserved state rather than continued polling of the old context.
How can I keep multiple workers from creating a retry storm?
Use a retry budget shared by workers or tenants, combine exponential backoff with random jitter, and honor provider retry delays. Stop issuing retries when the shared budget or workflow deadline is exhausted, even if an individual worker still has attempts available.
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