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Design an actor-based AI system by assigning each actor a clear owner, state boundary, and message protocol; keeping business-critical state outside the model; and putting durable orchestration, retries, authorization, and observability around model and tool calls. An actor can be an agent, but not every agent needs its own actor: make that choice when the agent needs isolated state, independent concurrency, or a durable identity. Use a workflow for known steps and an autonomous agent when the model genuinely needs to choose its next step.
What is an actor, and why use one for an AI agent?
An actor is an isolated unit of state and behavior with an identity and a mailbox. It receives messages, processes them serially, updates its own state, and sends messages to other actors. This creates explicit concurrency boundaries instead of letting multiple callers operate on shared mutable state.
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Actors are useful when work has identifiable owners and interactions: a conversation has session state, a job has progress and a deadline, a tenant has configuration and limits, and a tool adapter has a defined interface to an external service. Those boundaries can be independently addressed and scheduled. They do not, by themselves, make a distributed system correct or make an LLM’s answer trustworthy. Authorization, validation, duplicate handling, failure policy, and evaluation remain application responsibilities.
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Should each AI agent be an actor?
Only when the agent needs its own state, identity, concurrency boundary, or message contract. An agent is a reasoning role; an actor is a runtime and state-isolation boundary. They are related, but not interchangeable.
A request-based agent can be a short-lived operation within a conversation or job actor. A long-running autonomous agent may merit a durable actor identity so the system can address its task, track progress, and recover it. A specialist agent that has no independent state or lifecycle may instead be a worker invoked through a supervisor or workflow. Creating an actor for every prompt, tool call, or conceptual role adds message routing and lifecycle complexity without necessarily improving isolation.
Good reasons to create an actor
- Owned state: one conversation, job, tenant, or domain entity has state that should not be concurrently mutated by unrelated work.
- Independent progress: work should continue, retry, pause, or be cancelled separately from its caller.
- Explicit protocol: other components need a stable, typed way to request an operation and receive its outcome.
- Separate scaling or limits: the component needs its own concurrency controls, queue, or resource policy.
When another boundary is better
Do not make an actor merely because a prompt has a name such as “planner” or “researcher.” If a step is deterministic and has no independent state or lifecycle, a function or workflow step is often clearer. If multiple actors need to update the same business invariant, do not let them share mutable state; route commands to the actor or durable entity that owns that invariant.
How should actor boundaries and messages be designed?
Start with ownership and protocol, not with the number of agents. Each actor should own the state it can change and expose operations through messages that another component can validate and handle.
- Identify state owners. List the state that must survive a process restart or concurrent requests: for example, a conversation’s progress, a job’s status, or an order’s authorized transitions. Assign each item one clear owner.
- Separate domain entities from reasoning. An agent may interpret a request and propose an action, but a durable entity should own business invariants such as permissions, balances, or order state. The entity validates and applies a command rather than accepting an LLM’s direct mutation.
- Define message contracts. Specify the sender’s intent, required fields, expected result, and possible failure response. Version contracts so producers and consumers can evolve without silently changing what an old message means.
- Add execution metadata. Include a correlation ID to connect related work, a deadline to bound stale work, an idempotency key to identify retries of the same logical operation, and provenance so a decision can be traced to its initiating request or upstream result.
- Keep messages purposeful. Pass the minimum data a recipient needs, not an unrestricted conversation history or a mutable object shared by reference. Fetch or request additional information through an authorized interface when needed.
For example, an agent can send a proposed “place order” command to the order-owning entity. That entity checks the caller’s authority and the current order state, applies the command only if valid, and returns a result. The agent can explain or react to that result, but it is not the authority that decides whether the order is permitted.
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When should I use a workflow instead of an autonomous agent?
Use a workflow when the sequence is known in code and its steps need explicit retries or compensation. Use an autonomous agent when the model must decide what to consult, which specialist to delegate to, or whether to iterate. A bounded request-based agent is appropriate when the system needs one limited interaction rather than a durable, open-ended task.
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|---|---|---|---|
| Request-based agent | The application initiates a bounded interaction; the model reasons within that request. | A user needs an answer or action within a limited turn or task. | Bound tool calls, latency, and completion criteria; persist state elsewhere if it must outlive the request. |
| Workflow | Application code defines the order of steps. | Steps and transitions are known, and retries or compensation should be explicit. | Record step outcomes and define what happens when a step fails or an external side effect cannot be rolled back. |
| Autonomous agent | The model chooses among permitted next actions, consultations, or iterations. | The task requires dynamic tool choice, specialist delegation, or iterative investigation. | Persist task identity and progress, constrain actions and iterations, and supervise execution rather than relying on informal agent-to-agent calls. |
These patterns can coexist. A workflow may call a bounded agent for one reasoning step, then pass its structured proposal to a durable entity for validation. Conversely, an autonomous agent may request a supervisor to invoke a known workflow for a high-impact operation. The choice is about where control belongs, not whether a system is “agentic.”
What does a production actor architecture look like?
A useful baseline separates ingress, task control, reasoning, business state, and external effects. The supervisor or orchestration layer owns the task lifecycle; agent actors reason within a bounded scope; durable entities own invariants; and tool adapters mediate calls to external systems.
- Endpoint or event handler: authenticates the request, applies tenant-level limits, creates correlation metadata, and sends a command to the right task or conversation actor.
- Task actor or workflow: records task state, deadline, cancellation status, and completed steps. It controls whether work runs, retries, or ends.
- Agent actor: receives only the context needed for the current decision, produces a response or proposed action, and requests permitted tools through the orchestration boundary.
- Tool adapter: validates inputs, applies the actor’s authorization, handles provider-specific errors and timeouts, and returns a result in the application’s message contract.
- Durable entity: checks business rules and owns changes to critical state. It can reject a proposal even if it is well-formed or produced by a trusted model.
- Supervisor and audit path: enforces concurrency, retry, timeout, and escalation policy, and records message and state-transition outcomes.
This is a logical design, not a requirement that every role run as a separate process or deployment. A small system can keep several roles in one runtime while preserving their ownership and protocol boundaries. Split them operationally when independent scaling, isolation, deployment, or failure control justifies the extra distributed-system complexity.
How do I make messages and side effects safe to retry?
Assume crashes, duplicate delivery, timeouts, provider throttling, and partial success. A message can be delivered more than once, and a caller may time out after a remote service has acted but before the response arrives. Design consumers and side effects for that ambiguity.
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- Use idempotency keys: identify the logical operation so a repeated command does not create a second payment, order, or other duplicate effect. The receiving system or adapter must actually enforce the key; merely including it in a message is not enough.
- Make state transitions explicit: record whether a task is pending, running, completed, failed, or awaiting approval, and reject invalid transitions. This helps recovery distinguish unfinished work from work already applied.
- Bound queues and concurrency: set limits for queued messages and in-flight work. Without bounds, a slow model or provider can cause work to accumulate faster than workers can process it.
- Apply backpressure: slow, defer, or reject new work when the system approaches its capacity instead of allowing unbounded queue growth.
- Use timeouts and bounded retries: define how long each operation may wait and how many attempts are allowed. Exponential backoff can reduce pressure during transient failures, but retries should stop at a deadline or policy limit.
- Use circuit breakers where appropriate: temporarily stop calls to an unhealthy dependency rather than repeatedly sending requests that are likely to fail.
- Handle messages that cannot be processed: route exhausted or invalid work to a dead-letter or review path with its failure reason and correlation data, rather than silently discarding it.
- Compensate for non-transactional effects: when a multi-step operation cannot be committed atomically, define a compensating action or a human recovery path. Compensation is a new operation, not a guarantee that the original external effect can be erased.
At-least-once delivery requires duplicate-tolerant consumers. Do not imply exactly-once business outcomes just because a runtime provides durable execution or actor supervision: the effect may occur in an external system outside the runtime’s transaction boundary.
How should supervision and recovery work?
Put a supervisor above workers and make its response to failure explicit. Actor supervision assigns a parent responsibility for child failure, with policies such as restart, stop, or escalation; choose the policy according to what state is safe to reuse and what the task requires.
Restarting a worker can recover from a transient process failure, but it does not prove that an in-flight tool call did not complete. Before retrying, the task controller should consult durable progress and idempotency controls. Stop a worker when continuing would risk corrupt state or repeat an unsafe action; escalate when a human decision or higher-level policy is needed.
For long-running autonomous tasks, persist task identity and progress outside ephemeral model context. Record enough information to resume or safely abandon the task: current state, accepted results, pending operation, deadline, retry count, and relevant correlation identifiers. The exact persistence mechanism depends on the runtime and application; the essential requirement is that recovery does not depend on a vanished process or an unrecorded chat transcript.
How should AI context and memory be controlled?
Keep model context focused on the decision at hand, and store durable task or domain state outside the context window. The model should receive only the information needed for its current action, not unrestricted access to every session or tenant record.
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- Separate session memory from durable memory. Session history helps interpret an interaction; durable memory is application data that must be stored, governed, and retrieved deliberately. Do not treat text in a prompt as the authoritative copy of business state.
- Compact long histories. Summarize or compact old interactions when a session grows, while retaining the records needed for audit or recovery outside the prompt.
- Set budgets. Cap tool calls, reasoning iterations, and task duration. Model calls add latency and token cost, so an unbounded loop is both a reliability and a cost risk.
- Retrieve narrowly. Give an agent the data required for its current decision and use authorized retrieval for further information rather than embedding every available record in each request.
- Keep provenance. Record which request, tool result, or stored fact informed an action so operators can inspect the decision path.
Akka’s agent documentation covers session memory, long-term memory, and context compaction as separate concerns. That distinction is useful regardless of runtime: context is working material for a model call, while durable application state needs its own storage and access rules.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I secure actors, tools, and model outputs?
Enforce authorization at the boundary where an operation is requested and again where the protected state or external effect is owned. Actor isolation organizes state and concurrency; it does not automatically establish who may send a message or call a tool.
- Authenticate incoming requests and preserve tenant and caller identity in a trustworthy form.
- Authorize each tool operation against the actor’s identity, tenant, and current task, rather than granting every agent a universal tool set.
- Validate message schemas and structured model outputs, including values and allowed transitions. A schema constrains shape; it does not prove that a value is correct, safe, or authorized.
- Keep credentials and secrets out of prompts, logs, and broadly accessible messages. Tool adapters should obtain only the credentials they need.
- Treat retrieved text and model-generated content as untrusted input. Validate commands independently and do not use fluent model output as a substitute for policy enforcement.
- Require human approval for high-impact side effects when the risk warrants it, and record who approved what and when.
What should I observe in production?
Trace work across message handling, model calls, tools, retries, state changes, and approvals. A single model-call log cannot explain a distributed task if the surrounding messages and state transitions are invisible.
At minimum, monitor queue depth and message age, success and failure rates, retries, latency, token cost, cancellations, and escalations. Connect those signals to task and correlation identifiers so an operator can tell whether work is waiting, repeatedly failing, blocked on a provider, or awaiting a person. Keep audit records appropriate to the data and access rules of the application; observability should not become a reason to log secrets or unnecessary personal data.
OpenAI’s documented agent architecture distinguishes streaming events, webhooks, function handlers, and environment lifecycle. Those are useful operational surfaces to account for when using a managed harness: define which events your system consumes, where tool handling occurs, and how lifecycle or recovery state is reflected in your own monitoring.
How should I choose a runtime?
Choose based on control and recovery requirements, not on the word “agent” in a product name. Compare who sequences work, where durable state lives, how failures are supervised, what delivery guarantees apply, and who operates the deployment.
| Option | What the documentation emphasizes | Consider it when |
|---|---|---|
| Akka SDK | Actor semantics, agents and autonomous agents, workflows, durable entities, event sourcing, views, streaming, endpoints, timers, supervision, sharding, and cluster operation. | Actor ownership, durable state, and runtime-level supervision or clustering are central to the design. |
| OpenAI Agents API | A managed Codex harness with sessions, environments, tool handlers, events, streaming, webhooks, and recovery. | You want hosted orchestration and lifecycle management and are willing to shape the application around that managed boundary. |
| OpenAI Agents SDK | An application-run SDK that gives the developer more control over deployment, storage, approvals, and runtime integration. | You need to own more of the runtime and integrate orchestration with application-controlled infrastructure. |
| Direct Responses API integration | The cited material identifies it as a comparison point, but does not establish a feature-by-feature specification here. | Evaluate it against your required orchestration, persistence, approval, and recovery behavior before choosing it. |
For any candidate runtime, assess delivery semantics, backpressure, idempotency and compensation, model and tool routing, memory controls, auditability, deployment ownership, scaling and multi-region behavior, cost, governance, and evaluation support. The cited platform documentation describes capabilities and trade-offs, but does not provide independent numeric performance or market statistics to use as a universal ranking.
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How should I roll out an actor-based agent system?
Start with one bounded workflow and a small number of typed actors. Prove that ownership, recovery, and policy work before adding broad concurrency or autonomous delegation.
- Pick a constrained task. Choose an interaction with clear inputs, allowed tools, a completion condition, and a known owner for any state change.
- Write the protocol first. Define message versions, correlation IDs, deadlines, idempotency keys, provenance, and failure responses before connecting the model.
- Separate proposal from execution. Have the agent propose a structured action; route it through the entity or adapter that validates permissions and owns the side effect.
- Set bounds. Establish limits for queue depth, concurrency, model iterations, tool calls, retries, and task duration.
- Replay representative tasks. Use recorded or otherwise approved test tasks to assess behavior and cost; evaluate both successful outcomes and invalid or ambiguous requests.
- Inject failures. Test worker crashes, timeouts, duplicate messages, provider rate limits, and partial side effects. Confirm that recovery neither loses work silently nor repeats unsafe effects.
- Measure quality and operations. Track task success, failure modes, latency, cost, cancellations, and escalations, with traces that connect decisions to tool results and state transitions.
- Expand only after controls hold. Add parallel workers or autonomous delegation when the current system can enforce authorization, recovery, and audit across the new boundaries.
The resulting architecture is not “one actor per agent.” It is a set of explicit owners and protocols, with a deliberate controller for every unit of work and safeguards at every boundary where a model, message, or external system can affect state.
Frequently Asked Questions
Do actors guarantee exactly-once processing?
No. A runtime may persist execution or recover actors, but an external service can complete an operation just before a timeout or crash. Use idempotency at the point that performs the side effect and define how the application detects or resolves ambiguous outcomes.
Can an actor system prevent prompt injection?
No. Actor boundaries can restrict which component has access to a tool or data, but they do not make malicious or misleading input harmless. Treat model and retrieved content as untrusted, enforce policy outside the model, and limit tool permissions.
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That depends on the runtime’s execution model and desired concurrency. Whatever the implementation, make long-running calls cancellable or bounded, avoid blocking unrelated messages, and preserve task state so timeouts and restarts can be handled deliberately.
How do I test an autonomous agent before production?
Build an evaluation set that includes ordinary tasks, ambiguous requests, denied actions, tool failures, duplicate events, and interruption or cancellation. Compare outcomes against the permitted behavior and inspect traces, not just final text.
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