Start with the simplest design that meets the task’s quality requirements. Use a fixed workflow when the steps are predictable; give a model more freedom to choose tools or next steps only when adapting to the situation is valuable. Add parallel work, evaluation loops, or multiple agents only when testing shows they improve outcomes enough to justify their extra cost, latency, and coordination risks.
What makes an application an agent rather than a workflow?
The distinction is about who controls the next step. In a workflow, application code defines the route through models and tools. In an agent, the model dynamically decides what to do next, such as which tool to call or whether more information is needed. Many applications combine the two: code may enforce the overall process while a model makes bounded choices inside one step.
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Anthropic made this distinction in its December 2024 article, Building effective agents. The labels matter less than documenting which decisions are fixed in code and which are delegated to the model. That description makes a system easier to evaluate, secure, and explain than simply calling it “agentic.”
A useful starting point is a single model call augmented with retrieval, tools, or memory through clear interfaces. If that cannot meet the task’s requirements, add orchestration deliberately. The patterns below are design options, not a required maturity ladder or a formal industry-wide taxonomy.
Which design pattern fits the task?
Choose based on how predictable the task path is, whether the work can be split into independent parts, and how much control the task requires. These are qualitative tradeoffs: the cited sources do not provide a common benchmark that ranks all patterns.
| Pattern | Best fit | Who controls the path? | Main tradeoff |
|---|---|---|---|
| Augmented model | A task one model call can handle with retrieval, tools, or memory. | Mostly the application; the model responds within a bounded call. | Simple to inspect, but may not handle tasks that need several dependent steps. |
| Sequential workflow | A predictable series of model or tool steps whose intermediate results can be checked. | Code defines the order. | Control and debugging are straightforward; a fixed route may not adapt well to unexpected results. |
| Router or dispatch | Incoming requests differ enough to need specialized prompts, tools, or agents. | A classifier or model selects a route; code defines the available destinations. | Specialization can help match work to the right handler, but routing mistakes can send a task down the wrong path. |
| Parallel subtasks | Work can be separated into independent parts, or independent perspectives are useful. | Code or a coordinator defines the work and combines results. | Parts can run concurrently, but combining inconsistent or incomplete outputs takes care. |
| Evaluator-optimizer | There are explicit criteria for assessing and revising a candidate output. | A generator produces work; an evaluator checks it and may trigger revision. | Iteration can improve a candidate against stated criteria, at the cost of additional steps and possible unproductive loops. |
| Dynamic agent loop | The path cannot be specified well in advance and adapting tool use or next steps is important. | The model chooses among permitted actions and decides what to do next. | Adaptability comes with greater difficulty controlling, debugging, and predicting the run. |
| Multiagent coordination | Distinct responsibilities or parallel capacity justify delegation to bounded agents. | A coordinator assigns work and owns—or explicitly routes—the final decision. | Specialization may be useful, but coordination adds failure modes, latency, and authority questions. |
Anthropic’s 2024 article advises seeking the simplest effective solution and notes that agentic systems can trade latency and cost for task performance. Its reported experience was: “Consistently, the most successful implementations weren’t using complex frameworks or specialized libraries.” That is Anthropic’s account, not an independent industry-wide statistic. Its article also cautions that tooling details may change, so its value here is architectural rather than as current setup guidance.
When is a deterministic workflow sufficient?
A fixed workflow is a strong fit when the task has a known sequence, the application can identify the relevant branch in advance, and intermediate outputs can be validated. For example, an application might retrieve records, extract specific fields, validate them against a schema, and then produce a response. If those steps and their order are stable, giving a model open-ended control may add complexity without adding useful flexibility.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteUse a workflow when predictable behavior, straightforward debugging, or narrow permissions are especially important. It can still use models: the key is that code retains control of the sequence rather than asking a model to invent one at run time. A router can handle known variations by directing different request types to predefined paths.
Consider more autonomy when real tasks vary in ways that make a fixed path brittle—for example, when the next useful tool call depends on information found during the run. Do not infer that autonomy helps merely because the task sounds complex. Compare it against a simpler baseline on representative tasks, including failures and ambiguous inputs, and measure whether the added flexibility improves results enough to justify its operational costs.
How should teams evaluate a candidate architecture?
Evaluate complete tasks and execution trajectories, not just final text. A run may involve several model turns, tool calls, changes to state, and decisions based on intermediate results. A polished answer can conceal an incorrect action or a failed recovery; a trace can expose where the run went wrong. Anthropic’s evaluation guidance emphasizes making behavioral issues and changes visible before production and matching evaluation depth to system complexity.
Compare against a simple baseline
Keep the simplest viable design as a baseline. Test one architectural change at a time where practical, so the team can tell whether a router, evaluator, parallel branch, or agent loop is responsible for an improvement or regression. Use representative tasks rather than relying on a few successful demonstrations.
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- Task success and severity: Did the system complete the requested task, and how consequential were its mistakes?
- Latency and cost: What did the full run require, including retries, evaluation passes, and delegated work?
- Tool behavior: Were the right tools called with appropriate arguments, and did the system recover sensibly from tool errors?
- Consistency: Does behavior hold across representative variations, or does it depend on a narrow set of easy cases?
- Human oversight: How often did a person need to intervene or approve an action, and was that burden acceptable?
- Security and containment: Could the system be induced to take an unauthorized action, and how limited would the resulting damage be?
- Trace quality: Can reviewers see what the system did and understand why it took those steps?
Include failure-oriented cases
Alongside normal tasks, exercise ambiguous requests, malformed tool responses, unavailable tools, adversarial content, and consequential actions. These are practical test recommendations drawn from the concerns in Anthropic’s architecture, safety, and evaluation material; they are not a published benchmark or a claimed exhaustive test suite. Record intermediate decisions, tool inputs and outputs, state changes, failures, and approvals in a form reviewers can inspect.
What should be inside an agent’s boundary?
Review the system as four interacting parts: the model, its harness (instructions and guardrails), its tools, and its environment (the systems and data it can access). Anthropic’s April 2026 article, Trustworthy agents in practice, describes these parts together: “This is why the safeguards we and others build need to account for them all.” A capable model cannot compensate for an over-permissive tool or an exposed environment.
Limit tools and data to the task
Give the model only the tools and data its job requires. Make tool names, inputs, outputs, and effects explicit; distinguish reading information from changing it. Restricting access limits what can happen if the model misunderstands the request or is steered by untrusted content.
Match approval to consequence
Permission should depend on the action and its consequences. Read-only access may be acceptable without approval; sending a message, purchasing something, deleting data, or taking another consequential action may need a confirmation step. For a long-running task, reviewing a plan before execution can be more useful than approving every low-level operation, provided the user still has a meaningful way to intervene while the task runs. These are product choices described by Anthropic, not universal defaults: the appropriate checkpoint depends on the risk and use case.
Assume untrusted text can try to steer the model
Prompt injection is a concern when an agent processes untrusted content containing instructions. Treat text the agent reads as potentially adversarial, and avoid giving that text authority to expand the agent’s permissions. Anthropic describes layered mitigations that include training, monitoring, red teaming, restricting tools and data, and careful choice of operating environment. Those measures do not guarantee protection; containment still matters if an attempted attack succeeds.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When are multiple agents worth coordinating?
Delegation is most defensible when responsibilities are genuinely distinct, each delegate receives bounded inputs and produces checkable outputs, and a coordinator can handle disagreement or failure. Agents can work on separate subtasks, or one agent can invoke another through a defined input/output interface. Parallel capacity or specialization may justify the added machinery, but adding agents does not generally guarantee better accuracy.
Coordination becomes harder when agents act as long-lived peers with separate goals rather than as bounded, tool-like functions. Anthropic’s August 2026 research highlights uncertainty about real-world multiagent behavior and risks including confabulation and reward hacking; individual quirks can compound at system level. Keep authority narrow, specify who owns the final decision, and define what the coordinator should do when a delegate fails, returns unverifiable claims, or disagrees with another delegate. Treat any performance claim as specific to the task and implementation.
A practical architecture decision
- Write down the task and its quality bar. Identify what counts as completion, what errors matter most, and which actions have consequences.
- Map decisions to code or model. Keep predictable sequencing in code. Delegate only the decisions that benefit from contextual adaptation.
- Choose the smallest suitable pattern. Start with an augmented model or sequential workflow; add routing, parallel work, evaluation, or a dynamic loop only for a specific need.
- Define the operating boundary. Specify instructions, tools, accessible data, permissions, and human approval or intervention points.
- Test full runs against the baseline. Include ordinary tasks and failure-oriented cases, then compare success, severity, latency, cost, oversight burden, security, and trace quality.
- Keep added complexity only when evidence supports it. If coordination or autonomy does not improve the task enough to justify its costs and risks, return to the simpler design.
Anthropic’s architecture guide recommends beginning with single-purpose agents and increasing complexity as requirements evolve. Modular components such as reusable tools and prompts can help, but technical sophistication should remain proportional to business value.
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