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An AI agent is an acting component; agentic AI often describes a broader system that coordinates agents and manages a workflow. The distinction is useful, not universal: definitions vary, and agency can range from reactive assistance to systems coordinating work with limited human oversight. Orchestration can help when a complex task divides into meaningful specialties, but a single agent—or no agent at all—may be simpler and more effective for predictable work.
What are AI agents and agentic AI?
In practical terms, an agent uses a model, instructions and tools to carry out a task, often through a run-and-check loop that continues until an exit condition is reached. A multi-agent system distributes parts of that work among coordinated agents.
The OECD’s 2026 review describes “agentic AI” as most often referring to systems that integrate and coordinate multiple agents. It notes that individual agents without broader system-level orchestration generally are not called agentic AI under many definitions. But the field has no single settled vocabulary: the OECD presents agency as a spectrum, from reactive agents and copilot-like support to systems that coordinate agents and manage workflows with limited human oversight. Treat these as useful working distinctions, not a universal standard. OECD, The Agentic AI Landscape and Its Conceptual Foundations (2026).
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When should you use a single agent versus multiple agents?
Start with the smallest design that can complete the task reliably. A single agent with appropriately chosen tools may cover several steps without the coordination burden of multiple agents. Add specialists when the work genuinely separates into distinct, bounded subtasks—for example, independent analyses that can happen at the same time or work requiring different responsibilities.
- Choose a single agent or a conventional workflow when the task is predictable, highly structured, or can be handled in one model call. A fixed sequence of code-driven steps may be more cost-effective and easier to control.
- Consider multiple agents when subtasks are meaningfully distinct, can be assigned clear boundaries, and their outputs can be combined or handed off usefully.
- Do not assume more agents mean better results. Coordination introduces additional failure points, evaluation needs, access-control work, and computational overhead. The cited guidance does not establish a universal performance advantage for multi-agent systems.
Google Cloud similarly advises matching the design pattern to the task and notes that simple, predictable tasks may not need an agentic workflow. Google Cloud, Choose a Design Pattern for Your Agentic AI System.
How do orchestration patterns differ?
Orchestration is the way a workflow assigns work, controls its order, and decides who owns the result. Common patterns differ chiefly in how much control is fixed in code versus delegated to a model, and whether a coordinator or specialist is responsible for the final response.
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| Pattern | How it works | Best fit | Main trade-off |
|---|---|---|---|
| Single agent with tools | One agent selects tools and advances through a workflow until it reaches an exit condition. | Many tasks where one set of instructions and tools can cover the work. | Simple to start; a growing set of tools and instructions can become harder to manage. |
| Sequential | Steps run in a known order; each step’s output feeds the next. | Intake, processing and review stages with predictable dependencies. | Predictable, but the flow cannot flexibly skip or rearrange steps. |
| Concurrent | Independent subtasks run in parallel, then their results are combined. | Separate analyses that do not depend on one another. | Requires a reliable way to reconcile outputs and handle incomplete or conflicting results. |
| Manager with agents as tools | A manager calls specialists for bounded tasks and retains responsibility for the final response. | Work that benefits from specialist contributions while one component owns the answer. | The manager must choose and combine specialist results appropriately. |
| Handoff | Control transfers to a specialist that takes ownership of the next response. | A branch where another agent should take over, rather than merely return a result to a manager. | Routing and specialist responsibilities need to be narrow and clear. |
| Dynamic coordination | An agent plans and coordinates work without a fully predetermined sequence. | Open-ended tasks whose next steps depend on what is learned. | Planning and external actions need suitable controls; behavior is less predictable. |
| Hybrid flow | Different stages use different patterns, such as sequential intake followed by parallel analysis. | Workflows with both fixed dependencies and independent subtasks. | Combines the strengths of patterns but also their operational complexity. |
These patterns appear under different names and implementations. OpenAI’s Agents SDK documentation distinguishes agents-as-tools from handoffs and discusses model-led versus code-directed orchestration. Google Cloud describes sequential, hierarchical and multi-agent approaches. Microsoft Learn documents sequential, concurrent, group-chat, handoff and magentic patterns, and says patterns can be combined. These are framework-specific descriptions, not a controlled comparison of products.
- OpenAI Agents SDK: Agents and Orchestrating multiple agents.
- Microsoft Learn: AI Agent Orchestration Patterns.
How should you choose a design pattern?
- Write down the desired outcome and boundaries. Specify what counts as done, which actions are allowed, and which decisions require a person.
- Map the dependencies. If each known step relies on the previous one, use a sequential flow. If subtasks are independent, consider concurrent execution. If the next step depends on discoveries along the way, dynamic coordination may fit.
- Decide who owns the final answer. Keep a manager in charge when specialists should return bounded findings. Use a handoff when a specialist should take over the next response.
- Choose how much control to delegate. Code-directed transitions are more deterministic and can make speed, cost and performance easier to control. Model-led orchestration offers flexibility but requires careful evaluation of its decisions.
- Scope context and access. Give each agent only the data and tools its role needs. Define how outputs are passed between agents, and avoid granting a specialist broad permissions just because the manager has them.
- Test and observe the whole workflow. Evaluate routing, tool use, handoffs, final results and failure handling—not only the quality of each agent in isolation. Use structured outputs or code-directed transitions where predictable behavior matters.
- Add human review where actions need approval. For consequential or external actions, require an approval step rather than relying on coordination alone.
OpenAI recommends beginning with a single agent and adding complexity incrementally. Google Cloud’s guidance also cautions that simple predictable tasks may not need an agentic design. This makes orchestration an earned complexity: introduce it when a simpler design has a concrete limitation that the new pattern addresses.
What can a multi-agent research workflow look like?
Imagine a request to prepare a short, sourced briefing. A manager could split it into bounded tasks: one specialist gathers relevant sources, another analyzes a separate aspect of the question, and a third checks whether claims are supported. The first two tasks can run concurrently if they do not depend on each other; the manager then combines their findings, while a reviewer checks the resulting claims.
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This example illustrates a pattern, not a guarantee of higher accuracy. The workflow still needs clear source and output requirements, a way to handle disagreement or missing evidence, and evaluation of the final briefing. Parallel work can save elapsed time only when tasks are genuinely independent and coordination does not erase the benefit.
What changes when you add agents?
Every additional component creates more interfaces to define and more behavior to verify. Agents may pass incomplete context, choose an incorrect route, return conflicting findings, or fail to complete a subtask. Parallel or repeated model work can also raise computational costs. These risks make reliability, security and evaluation part of the architecture decision—not cleanup for later.
- Reliability: define timeouts, retries, completion conditions and what happens when a specialist fails.
- Security: scope each agent’s tools and data to its role; consider risks created by tool use and information passed between agents.
- Evaluation: test the workflow on representative cases, including ambiguous requests and failure paths, and monitor its behavior in use.
- Human oversight: use approval or feedback steps when a result or action needs review.
- Operating burden: account for coordination, observability, maintenance and model usage as well as the core task.
OpenAI’s orchestration guidance, Google Cloud’s design-pattern material and Microsoft’s framework documentation address different parts of these trade-offs; none is a universal benchmark showing one pattern wins for every workload.
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How should you assess framework examples?
Vendor documentation can show how a particular framework implements orchestration, but its feature names and availability are product-specific and may change. Compare implementations against the needs of your workflow rather than treating a documentation page as a ranking.
- Control: are transitions fixed in code, delegated to a model, or mixed?
- Ownership: does one manager retain the final response, or does a handoff transfer responsibility?
- Patterns: are sequential, parallel, handoff and dynamic flows supported in the form you need?
- Oversight: can people approve actions or provide feedback at the right points?
- Context and permissions: can data access and tool permissions be scoped to each role?
- Operations: what evaluation, observability, reliability and maintenance work will the system require?
Microsoft Learn describes human-in-the-loop approvals and feedback for its framework. Anthropic’s documentation describes a coordinator delegating parallel subtasks to specialized agents, but identifies the feature as beta and specifies a versioned beta header. Check current documentation and availability before relying on a vendor-specific capability. Anthropic Claude Platform Docs: Multiagent Orchestration.
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