The Tool Desk
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Actor frameworks are only one way to run production AI agents. The practical alternatives are explicit state graphs and workflows, lightweight agent SDKs, cloud-specific agent platforms, and durable-execution runtimes that sit underneath any of them. Choose among these layers by asking whether the path is known in advance, what state must survive a crash, how human approvals are recorded, and which language and cloud your team already operates.
For a short-lived task, a direct model loop or small SDK is usually enough. Known stages, compliance gates, or parallel branches favor deterministic code or a state graph. Runs that may wait hours for a person or survive deployment and provider failures need durable execution, regardless of which agent framework supplies the model and tools.
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What are the alternatives to an actor model for production AI agents?
An actor model gives each agent or component private state, a mailbox and message-driven concurrency. That can be useful, but it is not a requirement for reliable agents. Production systems commonly combine one of four approaches:
- Explicit graphs and workflow frameworks: engineers define nodes, edges, branches, joins and interrupt points.
- Lightweight agent SDKs: an application owns most of the loop while a library supplies tools, handoffs, guardrails, sessions and tracing.
- Cloud-specific frameworks and managed runtimes: orchestration, tools, memory, deployment and monitoring are integrated with a provider ecosystem.
- Durable workflow engines: a separate execution layer checkpoints progress, retries transient failures and resumes work after process or infrastructure loss.
These are not mutually exclusive. A state graph can call a lightweight SDK; a cloud framework can run inside a durable workflow; and a hand-written loop can be wrapped by a durable runtime. The important distinction is between agent policy (what the model may do) and execution reliability (what happens when the process, deployment or provider fails).
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When should I use a deterministic workflow instead of an agent loop?
Use deterministic code when the sequence, branching rules, parallel work, error handling or approval gates are known before execution. Use an agent loop when the task is open-ended and the model must choose which tools to call and in what order.
Deterministic workflows
In a deterministic workflow, your code defines control flow. A typical order might be: validate an intake record, retrieve policy documents, ask a model to extract fields, run a rules check, request approval, then write to a system of record. The model handles bounded reasoning inside a step; it does not decide whether a compliance gate can be skipped.
- Branches are visible in code or a graph and can be reviewed before deployment.
- Parallel work has an explicit fan-out and join point.
- Retries and error paths can be different for model calls, databases and irreversible tools.
- Approval pauses have a named location and a defined resume path.
- Auditors can inspect which version of each step ran.
Agent-directed loops
In an agent-directed workflow, the language model drives control flow by selecting tools or handing work to another agent. This is useful for research, troubleshooting and other tasks whose next action depends on information discovered at runtime. The application still needs hard boundaries: maximum turns, timeouts, permitted tools, token or cost limits, and a policy for refusing or escalating unsafe actions.
Microsoft’s agentic-pattern guidance describes these as two different patterns: “Deterministic workflows — Your code defines the control flow” and “Agent-directed workflows (agent loops) — The LLM drives the control flow.” Treat that distinction as an architectural decision, not a branding difference.
Which explicit state-graph frameworks are suitable?
State-graph frameworks make execution visible without requiring every component to be an actor. They are strongest when you need checkpointed state, replay, human-review interrupts or a graph that can be inspected during an incident.
LangGraph
LangGraph is positioned as an agent runtime for complex agents that need precision. Its graph model exposes nodes and edges, while stateful orchestration supports checkpointing, durable execution, replay and human-review interrupts. It is a fit when a team wants to see exactly where a run is, resume from a known point and keep model calls inside explicit steps.
The trade-off is design and operations work. Engineers must define state schemas, transitions, failure behavior and the storage used for checkpoints. A graph does not automatically make external side effects safe to repeat; payment, ticketing or data-write steps still need idempotency keys and careful retry policy.
Microsoft Agent Framework
Microsoft Agent Framework documents agents, workflows, orchestration, human-in-the-loop interactions, checkpoints, resuming and self-hosting. Its durable extensions are a natural path for .NET and Python teams already using Microsoft tooling, identity and deployment services.
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Portability should be tested rather than assumed. Check whether your tools, model providers, telemetry and state stores can move with you, and identify which features depend on Microsoft-managed services. A framework can be technically open while an application remains operationally tied to one cloud.
How a state graph differs from actors
| Concern | Actor-style coordination | Explicit graph or workflow |
|---|---|---|
| Control flow | Messages and actor behavior determine the next action. | Nodes, edges and branch conditions are declared. |
| State location | Often private actor state associated with an identity. | A run state object and checkpoints associated with a workflow execution. |
| Human approval | Usually implemented as a message or actor state transition. | Typically a named interrupt that records pending work and resumes at a defined node. |
| Debugging | Requires reconstructing message order and actor history. | The execution path and state transitions can be inspected directly. |
| Best fit | Large sets of independent, message-driven entities. | Known stages, branching, joins and review points. |
Is a role-based framework such as CrewAI a replacement for actors?
A role-based framework is an alternative coordination abstraction, not automatically a durability layer. CrewAI is described as a role-based multi-agent framework for fast prototyping, with flow-oriented options for more deterministic coordination. A “team of specialists” model can make responsibilities understandable to product teams and can speed up an initial application.
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When is a lightweight agent SDK the better choice?
Choose a lightweight SDK when the application should own dispatch and state, the workflow is short-lived, or you want a small abstraction over model and tool calls. OpenAI’s Agents SDK describes a deliberately small set of primitives: agents with instructions and tools, agents-as-tools or handoffs, and guardrails. It also provides sessions, human-in-the-loop mechanisms and built-in tracing.
Direct API loop versus an SDK
For a single request that invokes a few tools and completes within one process, a direct model API can be clearer. The application stores the transcript, validates tool arguments, chooses the next turn and decides when to stop. The SDK becomes useful when runtime-managed turns, tools, handoffs, sessions or resumable execution would otherwise become repetitive infrastructure.
The official guidance separates these choices: use lower-level API access when the application wants to own the loop for short-lived work; use the SDK when its managed turns and agent primitives reduce complexity. Neither option provides durable execution merely because it can call tools.
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Agents as tools versus handoffs
OpenAI’s orchestration guidance distinguishes two patterns:
- Agents as tools: a manager remains responsible for the user-facing result and invokes specialists as bounded capabilities.
- Handoffs: a specialist takes over the interaction and becomes responsible for the next part of the run.
Use the manager pattern when one policy owner should approve the final answer, aggregate evidence or enforce a common response format. Use handoffs when responsibility genuinely moves to a specialist, such as routing a support case to a domain-specific agent. In both cases, keep authorization and irreversible actions in application-controlled tools rather than relying on an instruction alone.
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Code-directed orchestration patterns
A lightweight SDK can still support explicit policy. Useful patterns include structured-output routing, chained stages, evaluator loops and parallel execution. For example, a classifier can return a typed route; code can then select one specialist, run independent retrieval tasks in parallel, and pass their outputs to a final synthesis step. The model supplies judgment inside each boundary while code controls sequencing and limits.
Do I need a durable runtime such as Temporal?
You need durable execution when a run must survive process loss, deployment, infrastructure failure, provider outage or a long wait for a person. You do not need it for every chat-like request. Durability is a separate production concern from the choice of agent framework.
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Microsoft’s Durable Task documentation describes the requirement plainly: agents that run for hours, call external tools and must survive infrastructure failures need automatic checkpointing and resume. A durable runtime records completed transitions, retries transient failures and continues from the last checkpoint instead of replaying the entire run.
Durable Task is explicitly not an agent framework. It can wrap Microsoft Agent Framework, LangChain, direct model API calls or another agent implementation. Temporal appears in comparison material in the same role: an execution substrate that supplies persistence and recovery while your SDK or application defines tools, prompts and policy.
Checkpoint boundaries and side effects
Durability does not mean every instruction is magically exactly once. Place model calls and external actions inside well-defined activities or steps. Make writes idempotent with a request or business key, record the provider response needed for replay, and separate a retriable computation from a non-retriable side effect.
- Safe to retry: a pure transformation, schema validation or a read with a stable snapshot.
- Needs idempotency: creating an invoice, sending an email, changing permissions or placing an order.
- Needs compensation: a sequence of actions where a later failure requires an explicit reversal.
Confirm how the selected runtime handles non-deterministic code, timers, version changes and in-flight executions before putting it in charge of production work.
What do cloud-specific agent frameworks add?
Cloud-native options reduce integration work when your data, identity, networking and deployment already live in one provider’s ecosystem. They are not proof of better model quality, and they can increase migration effort.
AWS Prescriptive Guidance groups Strands Agents, LangChain and LangGraph, CrewAI, Amazon Bedrock Agents and Amazon Bedrock AgentCore. It treats orchestration, tool integration, memory, workflow definition, deployment and monitoring as the core capabilities to compare. Microsoft Agent Framework and its durable extensions offer a similar stack-aligned path for Microsoft environments. Google ADK and other provider-focused options appear in comparison material for teams prioritizing a particular cloud.
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Questions to answer before choosing a managed path
- Can the application call models from more than one provider, and can prompts and tool schemas move without a rewrite?
- Where are transcripts, checkpoints, embeddings and tool results stored, and in which region?
- Can your security team inspect network paths, identity scopes and retention settings?
- Does tracing cover model calls, tool arguments, approvals, retries and state transitions?
- Can you self-host the same components for regulated or offline workloads?
- What is the migration plan if a managed feature changes or becomes unavailable?
Cloud affinity is a decision axis. Select it for integration, governance and operational fit, not because the provider label implies superior agent behavior.
How should I compare non-actor architectures?
Evaluate operational behavior rather than demo polish. A framework that produces an impressive conversation may still be difficult to resume, inspect or constrain under failure.
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|---|---|---|
| Control flow | Is sequencing code-defined, graph-defined or model-directed? | A visible trace of branches, loops, parallel tasks and stop conditions. |
| Durability | Where are checkpoints stored? What is the retry policy and resume granularity? | Kill a worker during a tool call and verify what reruns. |
| Human approval | Can any step pause, persist its request and resume after hours or days? | Restart the service while an approval is pending, then approve it. |
| State and memory | How are session transcripts, per-step state and long-term records separated? | Inspect retention, encryption, tenant isolation and schema migration behavior. |
| Interoperability | Which model providers, tool protocols and agent-to-agent mechanisms are supported? | Run the same tool schema against two providers and document changes. |
| Operations | Are tracing, replay, evaluation hooks, deployment and scaling available? | Trace one failed run from prompt through tool result and retry. |
| Team fit | Does it support your language, hosting model, identity and cloud constraints? | Deploy through the team’s normal CI/CD and security review. |
| Maturity and licensing | Is the release line maintained, and what depends on a managed service? | Review release history, support terms and the license for every required component. |
There is no credible universal winner for accuracy, latency, cost or adoption from the available comparison material. Those outcomes depend on models, prompts, tools, retrieval data, traffic and deployment. Run a workload-specific evaluation instead of treating framework popularity as a benchmark.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I choose an architecture step by step?
Use this sequence to avoid selecting a framework before defining the failure and control requirements.
- Describe the run boundary. Record the expected duration, number of model and tool calls, maximum acceptable cost, and whether the user must receive an answer in one request.
- Draw the control flow. Mark fixed stages, conditional branches, parallel work, loops and actions that require approval. Fixed paths point to code or a graph; genuinely open-ended paths point to an agent loop with limits.
- Classify state. Separate the conversation transcript, temporary step data, durable business records and secrets. Decide which of these must survive a process restart.
- Define failure semantics. For each model call and tool, specify timeout, retry count, backoff, idempotency key and compensation behavior. Do not leave these decisions to a default.
- Choose the smallest coordination layer that meets the need. Start with a direct loop or lightweight SDK for short-lived work; add a graph for visible stages and interrupts; add a durable runtime for long waits and recovery.
- Test an approval pause. Persist a pending approval, deploy a new version or terminate the worker, and verify that the run resumes at the intended point without duplicating side effects.
- Test provider and tool boundaries. Confirm that a model timeout, malformed tool argument, revoked credential and downstream outage each produce an actionable state rather than an infinite loop.
- Measure operations. Capture per-step latency, token use, tool errors, retries, human wait time and final outcomes. Compare architectures on the same workload and policy.
- Review portability and governance. Document cloud services, data regions, model dependencies, open-source licenses and the effort required to replace a component.
What does a production design look like without actors?
A practical design often has three layers:
1. Policy and orchestration
Use deterministic code or a graph for authentication, authorization, routing, approval gates, parallel joins and irreversible actions. Let the model propose an action, but let a typed tool and policy check decide whether it can execute.
2. Agent capability
Use a lightweight SDK or an agent framework for instructions, tool schemas, handoffs, guardrails, sessions and tracing. Keep the set of tools narrow and expose structured outputs where the next step is machine-controlled.
3. Durable execution
Use a durable workflow runtime when the run crosses process, deployment or human-wait boundaries. Store checkpoints and timers there, while business records remain in the system of record designed for them. This separation lets you change the agent framework without rewriting recovery semantics.
For a short support interaction, these layers may all live in one service and one request. For an approval-heavy claims process, they may span a graph, a queue, a durable workflow and several independently deployed tools. The architecture should grow with the failure modes, not with the number of agents in a diagram.
What are the common failure modes when replacing actors?
Assuming a graph is automatically durable
A visible graph can still lose in-memory state when its process exits. Verify checkpoint storage, serialization, versioning and resume behavior rather than inferring them from the graph API.
Using an agent loop for a fixed policy
If the model is asked to decide whether to run a required compliance check, a prompt change can alter the policy. Put mandatory sequencing and authorization in code or graph transitions.
Retrying side effects blindly
A retry after an uncertain network response can create a duplicate order or message. Use idempotency keys, query-before-create logic or a compensating transaction.
Persisting only the transcript
Conversation history does not tell you which tool effects completed, which approval was granted or which version of a policy was applied. Persist execution state and business events separately.
Letting approvals live in a browser tab
Human approval must be a durable record linked to the run, actor, decision, timestamp and policy version. A lost tab or expired session should not erase the pending action.
Confusing tracing with replay
A trace helps explain what happened; replay requires stored inputs, state and deterministic boundaries. Confirm whether a platform can reproduce a step or only display telemetry.
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Which option should I select for a new project?
Use the following practical mapping:
| Project condition | Starting point | Add when requirements grow |
|---|---|---|
| One model, a few tools, completion in one request | Direct API loop or lightweight agent SDK | Sessions, guardrails and tracing as needed |
| Known stages, approvals or compliance gates | Deterministic code or explicit state graph | Checkpoint storage and durable execution for long waits |
| Open-ended investigation with bounded authority | Agent loop with strict turn, tool and timeout limits | Structured routing, evaluator loops and durable checkpoints |
| Many specialist roles and fast prototyping | Role-based framework or flow abstraction | Independent verification of persistence, deployment and observability |
| Hours-long runs, outages or human pauses | Any suitable agent layer plus a durable workflow runtime | Idempotency, compensation and versioning tests |
| Deep investment in one cloud | That provider’s framework and managed runtime | Portability tests, data-boundary review and an exit plan |
The safest default is not “more agents.” It is explicit ownership of control flow, state and side effects, with model-directed behavior used only where uncertainty creates value. Add a durable runtime when the business process must outlive the process that started it.
Frequently Asked Questions
Can I combine LangGraph or an agent SDK with Temporal or Durable Task?
Yes. Treat the graph or SDK as the agent layer and the durable engine as the execution layer. Define clear activity boundaries, persist the inputs needed for replay, and make external writes idempotent so a recovery does not duplicate them.
What state should an AI agent persist during a human approval pause?
Persist the workflow run identifier, current step, requested action, structured arguments, relevant evidence, policy version, approver identity and an expiration or cancellation rule. Keep secrets and large documents in their proper stores, referenced by durable identifiers rather than copied into an approval record.
How can I migrate from an actor-based agent without rewriting every tool?
Keep tool contracts and authorization checks independent of the actor transport. Replace mailbox messages with graph transitions, SDK handoffs or workflow activities, then add an adapter that maps the old actor state to the new run state while both paths are validated on the same test cases.
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No. It guarantees recovery behavior according to its checkpoint and retry model, not universal exactly-once effects from external systems. Cache or record completed model calls where appropriate, and design every non-idempotent tool with deduplication or compensation.
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