To keep an agent’s work safe when a connection falters, save accepted inputs and progress to durable local storage before acknowledging them, and place network-dependent actions in a persistent outbox. That preserves what the agent knows and intends to do; it does not make remote models, APIs, or cloud tools usable offline.
What “keep agent work local” can—and cannot—mean
A network brownout is an unreliable or interrupted connection, not necessarily a complete outage. Local continuity means the agent can still access its saved state, code, and any tools or model it needs without the network. Hosted recovery means a service can restore persisted workflow state after a process or infrastructure failure. These are different capabilities: a cloud checkpoint may help recover a session after a worker stops, but it cannot supply a remote model call while the client is disconnected.
Separate the work into two records: a durable local journal for accepted inputs and progress, and an outbox for actions that still need to reach a server. The journal answers “what did the agent accept and complete?” The outbox answers “what did it intend to send, and what is the delivery status?”
Build local durability around a journal and an outbox
Save before acknowledging
Persist the user request and enough task context to explain or resume it before telling the user the agent has accepted the work. Record completed-step markers and relevant outputs as execution proceeds. A process’s in-memory task list is not durable: it can disappear on a crash, app termination, or device restart.
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Android’s offline-first guidance recommends a local data source as the source of truth for higher layers, with local-first writes and later synchronization. This is a transferable design pattern, not a requirement to use Android libraries. For an agent, it means local reads should be able to show the saved request and its known progress even if the network-backed service is unavailable. See Android’s offline-first architecture guidance.
Make pending actions recoverable
Before attempting a network-bound action, write an outbox entry with a stable identifier, the intended operation and payload or a secure reference to it, its current status, and retry or result metadata. Use explicit states such as pending, accepted, completed, and failed; distinguish a request merely queued locally from one acknowledged by the server. Persist the entry before sending it, so a crash during transmission does not erase the intent.
On reconnect, drain the outbox in a controlled order. Retry transient failures with bounded backoff, but surface authorization failures and other terminal errors instead of retrying them indefinitely. Where the action may have reached the server before the connection dropped, query its status or retry with an idempotency key or equivalent deduplication mechanism. A durable queue helps recovery, but it does not guarantee exactly-once execution.
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Define how local and server changes reconcile
Local and server copies can diverge while disconnected, especially when another device or service edits the same record. Version numbers or timestamps can help detect concurrent changes, but detection is not a conflict policy. Decide whether to ask the user, merge fields, reject a stale update, or apply last-write-wins; the last option can silently discard an earlier edit. Make the chosen outcome visible.
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Decide what can actually run without a network
Saved conversation history, prompts, and workflow checkpoints preserve context, not execution capability. Remote model inference, cloud APIs, and hosted tools still require connectivity. An agent can continue only the steps supported by its local code, data, model, and tools; mark remote-dependent steps as waiting and resume them when the connection returns.
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Before relying on offline continuation, inventory each step’s dependencies: the model endpoint, APIs, credentials, files, and tools it needs. Store sensitive data with suitable access controls and retention rules. Large files and tool outputs may need a different storage policy from compact workflow state. If a credential expires or a service rejects a request, waiting for the network alone will not resolve that failure.
Choose the durability boundary that fits the failure
| Approach | Where it runs | What can remain available offline | Durability and recovery boundary | Main trade-off |
|---|---|---|---|---|
| Local journal and outbox | On the device or local process, backed by local storage | Saved state and locally available code, model, and tools; remote-dependent steps wait | Can survive process restart if persisted; device loss requires a separate backup or replication strategy | Requires local storage, security, retention, retry, and conflict policies |
| Database client offline mode | Application using a specific database SDK | That SDK’s supported cached data and queued operations, not arbitrary agent execution | Depends on the client’s persistence configuration and documented scope | Behavior is product-specific; do not generalize it to unrelated calls or tools |
| Hosted durable execution | Self-hosted worker or managed cloud service | Persisted session or workflow state may be recoverable; disconnected clients still cannot invoke unreachable remote dependencies | Depends on the framework’s persistence implementation and hosting configuration | Needs service connectivity and operational infrastructure; checkpointing is not local offline execution |
Choose the boundary based on the failure you need to survive. A local database can protect work from a process restart on the same device; it does not protect against device loss unless state is copied elsewhere. A remote durable store can help recover after a worker or host fails, but it depends on reaching that service and does not make remote inference available in a brownout.
What platform examples establish
Android offline-first architecture
Android’s guidance covers local sources of truth, queued writes, retry behavior, network monitoring, and conflict resolution. Its synchronization examples can defer work until connectivity is available and use persistent work. Those are useful implementation patterns; the specific Android facilities are not agent guarantees.
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Firebase Realtime Database on Android
Firebase documents local disk persistence, cached data, and persistence of its write queue across app restarts when disk persistence is enabled. This describes the Android database client’s supported operations, not a general-purpose agent journal or proof that arbitrary tool calls survive. See Firebase’s Android offline capabilities documentation.
Microsoft Agent Framework and Foundry
Microsoft’s Agent Framework Durable Extension describes persisting agent sessions, checkpointing workflow progress, and recovering from failures, with Azure Functions and self-hosted hosting models. The storage documentation also distinguishes local session state from service-managed conversation storage; local session state alone does not establish disk durability unless an appropriate persistence implementation is configured. See the Durable Extension documentation and the session storage documentation.
Microsoft Foundry’s hosted-agent resilience documentation is labeled preview and describes durable work identity, persisted inputs, lease-based recovery, and stream replay. It explicitly does not promise deterministic replay or restoration of local variables and an in-memory call stack. Preview behavior can change; verify the current documentation and product availability before depending on it. See Foundry hosted-agent resilience.
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Durable messaging and event logs
AWS’s Well-Architected Agentic AI Lens warns that messaging infrastructure without persistence can make recovery impossible after a component outage. That supports using durable messaging for asynchronous work; it does not mean cloud messaging stays reachable during a network outage. See the AWS Agentic AI Lens.
Stately’s documentation describes an event log as the durable artifact and snapshots as a cache, but its cited page is on the project’s next branch and may change. The openJII offline outbox ADR offers a concrete project example: it records required content before field work and saves accepted records in SQLite before enqueueing them for upload. Neither example defines a universal agent format. See Stately Agent persistence and the openJII offline outbox decision record.
Quick Recap
A practical recovery sequence
- Accept locally: write the request and required task context to durable storage, then acknowledge acceptance.
- Track progress: persist completed-step markers and relevant results as they occur; mark a remote-dependent step as waiting when connectivity fails.
- Queue intent: record each network action in the outbox with a stable identifier and pending status before attempting delivery.
- Re-establish status: when the connection returns, determine whether an in-flight operation completed before the disconnect. Query its result or use deduplication before sending again.
- Retry selectively: apply bounded backoff to transient errors; report authorization, validation, and other terminal errors for resolution.
- Reconcile: compare versions or other conflict signals, apply the stated policy, and show the user what changed or remains unresolved.
- Resume honestly: continue only the steps whose required model, tools, credentials, and services are available; do not present a queued action as completed.
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