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Use deterministic code when a step has a known input, a known rule and a known outcome; call an LLM when the task depends on interpreting ambiguous requests, unstructured information or choices that cannot be specified in advance. Many strong agent architectures use both: code for validation, routing and execution, and a model only where reasoning adds value.
What does it mean to “skip” the LLM?
It means not making a model call for every operation in an AI-powered system. A program can check required fields, apply a fixed policy, transform data, route a request along a known path or execute an approved action without asking a language model to decide how.
Terminology varies across providers. Anthropic distinguishes workflows, where models and tools follow predefined code paths, from agents, where a model dynamically directs its process and tool use. Its article was published December 19, 2024, and notes that some tooling details may have changed. Google Cloud likewise describes deterministic workflows as predictable sequences whose steps are known in advance and change little between runs.
The useful distinction is not whether a system uses AI at all. It is whether a particular decision needs model reasoning or can be handled more reliably by an explicit rule.
When is deterministic code the better choice?
Prefer code or rules when the correct path can be specified ahead of time. OpenAI says agents are suited to complex decisions, difficult-to-maintain rules and workflows that rely heavily on unstructured data; otherwise, a deterministic solution may be enough. Microsoft’s AI Decision Framework recommends deterministic code for known tasks, while AWS advises using AI where it provides value that deterministic code cannot.
- Validation: Check that required fields exist, dates are valid or values fall within permitted ranges.
- Fixed routing: Send a request to a destination based on an explicit category, account type or policy.
- Transformation: Convert a known input format into a known output format using defined rules.
- Permissions and execution: Enforce access checks and carry out approved actions through controlled code.
- Stable procedures: Run known steps in a fixed order, or execute independent branches in parallel.
For these tasks, coded logic is typically easier to test and inspect. AWS identifies faster execution, lower cost, improved testability and simpler threat modeling as potential benefits of using more coded logic. These are architectural advantages, not a guarantee that every implementation will be faster or cheaper.
When does a task justify an LLM or agent?
A model becomes useful when the system must interpret information that does not fit a reliable set of rules, or choose what to do next based on the situation. Examples include interpreting a loosely worded request, extracting meaning from documents or planning a sequence of actions across tools when the right sequence depends on what each step finds.
A single LLM agent can be appropriate when a request involves multiple steps, external information and model-directed planning or tool choice. Google Cloud recommends beginning with a single agent before adding more moving parts. An agent is not automatically the right solution simply because a task has several steps: if those steps and their order are stable, a fixed workflow may be simpler.
How should you choose an architecture?
Use these questions to decide whether a step belongs in code, a fixed workflow or a model-directed process:
- Can you define the inputs, rules and correct outcomes? If yes, implement the step in code or a predefined workflow where practical.
- Does the task depend on ambiguity or unstructured information? If it does, a model may help interpret the input. Keep subsequent checks explicit where possible.
- Must the system choose actions dynamically? Use an agent when the appropriate next step depends on information gathered during the task, rather than a sequence known in advance.
- What happens if the system is wrong? The more consequential the action, the more important it is to validate outputs, limit permissions and make execution controllable.
- Would multiple agents have distinct responsibilities? Add them only when the work can be meaningfully divided and the benefits justify the extra coordination and oversight.
Google Cloud’s architecture guide compares sequential and parallel workflows with single- and multi-agent patterns. A fixed sequence or parallel branches can avoid model-based orchestration when the steps are known, potentially reducing latency and operating costs at the cost of flexibility. The best fit depends on how much the task actually changes between runs.
What does a hybrid system look like?
A practical design gives the model a bounded reasoning role and keeps predictable operations in code. Microsoft’s framework says not every step in an agent architecture warrants an LLM call; AWS recommends coded logic for deterministic execution and AI where it adds value that code cannot.
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- Use a model for the uncertain part. Ask it to interpret intent or summarize unstructured content when fixed rules are insufficient.
- Validate the model’s result. Check its output against a schema, business rules and permitted values before using it.
- Route and act through controlled code. Enforce permissions and execute only approved operations. Where model output can select tools, restrict access with allow lists.
- Handle failures explicitly. Define what happens when the model’s output is incomplete, invalid or uncertain rather than letting an unchecked result flow into an action.
This separation makes responsibilities clearer: the model interprets or plans, while the program enforces rules and carries out constrained actions. AWS’s guidance is to “Implement coded logic for deterministic execution. Use AI functionality where it provides value that deterministic code cannot.”
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When are multiple agents worth the added complexity?
Multiple agents can make sense when a system has distinct specialist responsibilities that can be separated cleanly. Google Cloud notes possible modularity and scalability benefits, but also warns that multi-agent systems add evaluation, security, reliability, coordination and cost considerations compared with single-agent systems.
Before splitting one agent into several, check whether a normal function, fixed workflow step or single agent can do the job. If specialists are warranted, define their responsibilities, allowed tools and handoffs clearly, then evaluate the complete system—not just each agent in isolation. OpenAI’s Agents SDK documentation also describes orchestration approaches for multi-agent systems.
Does “10,000 agents, zero tokens” describe a proven architecture?
No literal scale result follows from the architectural guidance cited here. These sources support the principle that predictable work often belongs in deterministic code and that model calls should be reserved for tasks that benefit from interpretation or dynamic reasoning. They do not establish that a particular system ran 10,000 agents, used zero tokens, or demonstrated a measured outcome at that scale.
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Quick Recap
Sources and further guidance
- OpenAI: A practical guide to building agents
- Google Cloud: Choose a design pattern for your agentic AI system
- Microsoft Learn: Agent search and tool use architectures
- Anthropic: Building Effective AI Agents
- Microsoft AI Decision Framework: Capability Model
- AWS Prescriptive Guidance: System design and security recommendations for agentic AI systems
- OpenAI Agents SDK: Agent orchestration
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