Introduction
Multi‑agent systems have become a cornerstone of modern artificial intelligence, enabling complex tasks to be broken down into smaller, specialized components that can operate concurrently or in coordination. The rise of large multimodal foundation models, such as Amazon Nova, has amplified the potential of these systems, allowing agents to understand and generate text, images, code, and more. However, harnessing the full power of these models requires a clear understanding of how agents can collaborate, share knowledge, and orchestrate workflows.
The AWS Strands Agents SDK provides a lightweight, open‑source framework that abstracts many of the low‑level details of agent communication, state management, and task delegation. By pairing this SDK with Amazon Nova’s multimodal capabilities, developers can rapidly prototype sophisticated multi‑agent architectures without getting bogged down in networking or data serialization concerns. In this post we dive into four distinct collaboration patterns—Agents as Tools, Swarms Agents, Agent Graphs, and Agent Workflows—exploring the scenarios where each shines, the trade‑offs involved, and concrete guidance on how to implement them using Strands Agents.
Understanding these patterns is not merely an academic exercise; it directly impacts the scalability, maintainability, and performance of real‑world applications ranging from automated content creation pipelines to autonomous scientific discovery systems. By the end of this article you will have a practical toolkit for selecting the right collaboration strategy and a clear roadmap for bringing it to life with Strands Agents and Amazon Nova.
Main Content
Agents as Tools
When a single agent’s expertise is sufficient to solve a sub‑problem, treating that agent as a reusable tool can dramatically simplify the overall system. In this pattern, a primary orchestrator sends a request to a specialized agent, receives a response, and then integrates that response into a larger workflow. The key advantage is modularity: each tool can be swapped out for a different model or algorithm without affecting the orchestrator’s logic.
Implementing this pattern with Strands Agents is straightforward. The orchestrator defines a tool interface that specifies the input schema and the expected output. A Nova‑powered agent is instantiated with a prompt that encapsulates the domain knowledge, and the SDK handles the HTTP request/response cycle. Because the tool is stateless, the orchestrator can invoke it repeatedly across multiple tasks, enabling parallel execution and reducing latency. A practical example is an image captioning pipeline where a Nova agent generates descriptive text for each image, and the orchestrator aggregates captions into a final report.
Swarms Agents
Swarms represent a collective of agents that operate in parallel, each exploring different hypotheses or strategies. This pattern is particularly effective for search‑intensive problems, such as hyperparameter tuning or exploratory data analysis, where diversity of solutions can accelerate convergence.
With Strands Agents, a swarm is managed by a leader agent that distributes tasks to worker agents. Each worker receives a slightly varied prompt or a different subset of data, processes it independently, and returns its findings. The leader aggregates the results, applies a voting or ranking mechanism, and may spawn additional workers if the solution space remains uncertain. Amazon Nova’s multimodal strengths allow workers to interpret complex inputs—such as combining textual prompts with visual cues—making swarms especially powerful for multimodal reasoning.
A real‑world use case is a scientific literature review system. The leader agent parses a list of research papers, dispatches worker agents to extract key findings and methodologies, and then synthesizes a comparative analysis. Because each worker operates concurrently, the overall review time drops from days to hours.
Agent Graphs
Agent graphs model a directed acyclic graph (DAG) of agents where the output of one node becomes the input of another. This pattern excels when tasks have clear dependencies but still benefit from parallelism. Unlike swarms, which focus on exploring variations, graphs emphasize structured data flow.
Strands Agents supports graph construction by allowing developers to declare nodes and edges declaratively. Each node is an agent that can be instantiated with a Nova model tailored to its role—data cleaning, feature extraction, or decision making. The SDK’s scheduler ensures that a node only executes once all its upstream dependencies have completed. This guarantees consistency while still leveraging Nova’s parallel inference capabilities.
Consider a content moderation pipeline: a text‑analysis agent flags potentially harmful language, an image‑analysis agent detects visual violations, and a final decision agent merges the two signals to produce a compliance verdict. By arranging these agents in a graph, the system can process text and image streams simultaneously, reducing overall latency.
Agent Workflows
Workflows combine the strengths of the previous patterns into a higher‑level orchestration that can adapt to dynamic conditions. A workflow may invoke tools, launch swarms, or traverse a graph based on runtime data. This flexibility is essential for end‑to‑end systems that must respond to user input, external events, or internal state changes.
Implementing a workflow with Strands Agents involves defining a state machine where each state corresponds to an agent or a sub‑workflow. The SDK’s event bus allows agents to emit signals that trigger state transitions. Amazon Nova’s multimodal output can be used to inform decisions—for example, a visual cue from a Nova agent might prompt the workflow to spawn a swarm of agents for deeper analysis.
An illustrative example is a personalized learning platform. The workflow starts by assessing a student’s knowledge through a Nova‑powered quiz agent. Depending on the results, the system may launch a swarm of tutoring agents to generate practice problems, or it may route the student to a graph of content modules that adapt to their learning style. The workflow’s adaptive nature ensures that each learner receives a tailored experience.
Conclusion
The four collaboration patterns—Agents as Tools, Swarms Agents, Agent Graphs, and Agent Workflows—offer a spectrum of strategies for building robust, scalable, and intelligent multi‑agent systems. By leveraging the AWS Strands Agents SDK and Amazon Nova’s multimodal prowess, developers can rapidly prototype and iterate on these patterns without wrestling with low‑level infrastructure concerns. Whether you need a simple tool invocation, a parallel search, a structured data pipeline, or an adaptive end‑to‑end system, there is a pattern that aligns with your goals. The key lies in understanding the problem’s structure, the dependencies between tasks, and the desired level of parallelism.
As the field of multimodal AI continues to evolve, the ability to orchestrate diverse agents will become increasingly valuable. Strands Agents provides the plumbing, while Nova supplies the intelligence—together they form a powerful duo for tackling tomorrow’s complex challenges.
Call to Action
If you’re ready to experiment with multi‑agent collaboration, start by cloning the Strands Agents repository and exploring the sample projects that demonstrate each pattern. Try replacing the default Nova prompts with your own domain‑specific instructions to see how quickly the system adapts. Join the Strands community on GitHub and Discord to share ideas, ask questions, and contribute improvements. By building and sharing your own multi‑agent applications, you’ll help shape the next generation of intelligent systems that can reason, collaborate, and learn together.