mirror of https://github.com/dapr/dapr-agents.git
84 lines
6.6 KiB
Markdown
84 lines
6.6 KiB
Markdown
# Core Principles
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## 1. Agent-Centric Design
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Dapr Agents is designed to place agents, powered by LLMs, at the core of task execution and workflow orchestration. This principle emphasizes:
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* LLM-Powered Agents: Dapr Agents enables the creation of agents that leverage LLMs for reasoning, dynamic decision-making, and natural language interactions.
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* Adaptive Task Handling: Agents in Dapr Agents are equipped with flexible patterns like tool calling and reasoning loops (e.g., ReAct), allowing them to autonomously tackle complex and evolving tasks.
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* Seamless Integration: Dapr Agents’ framework allows agents to act as modular, reusable building blocks that integrate seamlessly into workflows, whether they operate independently or collaboratively.
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While Dapr Agents centers around agents, it also recognizes the versatility of using LLMs directly in deterministic workflows or simpler task sequences. In scenarios where the agent's built-in task-handling patterns, like `tool calling` or `ReAct` loops, are unnecessary, LLMs can act as core components for reasoning and decision-making. This flexibility ensures users can adapt Dapr Agents to suit diverse needs without being confined to a single approach.
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!!! info
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Agents are not standalone; they are building blocks in larger, orchestrated workflows.
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## 2. Decoupled Infrastructure Design
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Dapr Agents ensures a clean separation between agents and the underlying infrastructure, emphasizing simplicity, scalability, and adaptability:
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* Agent Simplicity: Agents focus purely on reasoning and task execution, while Pub/Sub messaging, routing, and validation are managed externally by modular infrastructure components.
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* Scalable and Adaptable Systems: By offloading non-agent-specific responsibilities, Dapr Agents allows agents to scale independently and adapt seamlessly to new use cases or integrations.
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!!! info
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Decoupling infrastructure keeps agents focused on tasks while enabling seamless scalability and integration across systems.
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## 3. Modular Component Model
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Dapr Agents utilizes [Dapr's pluggable component framework](https://docs.dapr.io/concepts/components-concept/) and building blocks to simplify development and enhance flexibility:
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* Building Blocks for Core Functionality: Dapr provides API building blocks, such as Pub/Sub messaging, state management, service invocation, and more, to address common microservice challenges and promote best practices.
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* Interchangeable Components: Each building block operates on swappable components (e.g., Redis, Kafka, Azure CosmosDB), allowing you to replace implementations without changing application code.
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* Seamless Transitions: Develop locally with default configurations and deploy effortlessly to cloud environments by simply updating component definitions.
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* Scalable Foundations: Build resilient and adaptable architectures using Dapr’s modular, production-ready building blocks.
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!!! info
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Developers can easily switch between different components (e.g., Redis to DynamoDB) based on their deployment environment, ensuring portability and adaptability.
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## 4. Actor-Based Model for Agents
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Dapr Agents leverages [Dapr’s Virtual Actor model](https://docs.dapr.io/developing-applications/building-blocks/actors/actors-overview/) to enable agents to function efficiently and flexibly within distributed environments. Each agent in Dapr Agents is instantiated as an instance of a class, wrapped and managed by a virtual actor. This design offers:
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* Stateful Agents: Virtual actors allow agents to store and recall information across tasks, maintaining context and continuity for workflows.
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* Dynamic Lifecycle Management: Virtual actors are automatically instantiated when invoked and deactivated when idle. This eliminates the need for explicit creation or cleanup, ensuring resource efficiency and simplicity.
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* Location Transparency: Agents can be accessed and operate seamlessly, regardless of where they are located in the system. The underlying runtime handles their mobility, enabling fault-tolerance and dynamic load balancing.
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* Scalable Execution: Agents process one task at a time, avoiding concurrency issues, and scale dynamically across nodes to meet workload demands.
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This model ensures agents remain focused on their core logic, while the infrastructure abstracts complexities like state management, fault recovery, and resource optimization.
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!!! info
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Dapr Agents’ use of virtual actors makes agents always addressable and highly scalable, enabling them to operate reliably and efficiently in distributed, high-demand environments.
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## 5. Message-Driven Communication
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Dapr Agents emphasizes the use of Pub/Sub messaging for event-driven communication between agents. This principle ensures:
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* Decoupled Architecture: Asynchronous communication for scalability and modularity.
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* Real-Time Adaptability: Agents react dynamically to events for faster, more flexible task execution.
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* Seamless Collaboration: Agents share updates, distribute tasks, and respond to events in a highly coordinated way.
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!!! info
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Pub/Sub messaging serves as the backbone for Dapr Agents’ event-driven workflows, enabling agents to communicate and collaborate in real time.
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## 6. Workflow-Oriented Design
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Dapr Agents embraces workflows as a foundational concept, integrating [Dapr Workflows](https://docs.dapr.io/developing-applications/building-blocks/workflow/workflow-overview/) to support both deterministic and event-driven task orchestration. This dual approach enables robust and adaptive systems:
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* Deterministic Workflows: Dapr Agents uses Dapr Workflows for stateful, predictable task sequences. These workflows ensure reliable execution, fault tolerance, and state persistence, making them ideal for structured, multi-step processes that require clear, repeatable logic.
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* Event-Driven Workflows: By combining Dapr Workflows with Pub/Sub messaging, Dapr Agents supports workflows that adapt to real-time events. This facilitates decentralized, asynchronous collaboration between agents, allowing workflows to dynamically adjust to changing scenarios.
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By integrating these paradigms, Dapr Agents enables workflows that combine the reliability of deterministic execution with the adaptability of event-driven processes, ensuring flexibility and resilience in a wide range of applications.
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!!! info
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Dapr Agents workflows blend structured, predictable logic with the dynamic responsiveness of event-driven systems, empowering both centralized and decentralized workflows.
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