General agentic design patterns implemented on OCI

Agentic design involves creating operational and behavioral blueprints that serve as a basis for developing single- and multi-agent systems. Thanks to well-planned design frameworks, agents can reason, plan, access appropriate tools for task execution, and take definite action to achieve desired outcomes. Solution architecture helps bring agentic design to life. Its main components are:

• Agent frameworks like LangChain, LangGraph, CrewAI, Microsoft AutoGen, and others used to construct the agent life cycle, manage memory and state, and define linear and complex workflows.
• Large language models (LLMs) that enable assistants to understand and generate natural language to support reasoning, decision-making, and task completion.
• Retrieval Augmented Generation (RAG) pipelines that connect LLMs with up-to-date data sources to produce more accurate and higher-quality outputs.
• Tool integration that enables AI agents to access external APIs, apps, web searchers, and databases for intricate task execution.
• Instructions and orchestration that allow agentic systems to fulfill commands, handle complex workflows and task dependencies, and collaborate through inter-agent communication.

Strong agentic design patterns and architecture pave the way for adaptable, scalable, and efficient AI assistants that learn from their experience and expand with your business needs, improving accuracy and driving productivity around the clock.

Whether deploying single- or multi-agent systems, the OCI platform is the ideal option, offering cost-efficiency, low latency, and scalability by default. A single-agent architecture involves one agent that performs linear steps to reach the established goal. The most common single-agent design patterns that can be implemented on OCI include:

• Reflection: AI agents continuously review and criticize their own outputs to find mistakes, improve inefficiencies, and deliver error-free results. This blueprint is especially critical for assignments like adaptive problem-solving, content creation, and customer personalization.
• Tool Use: LLMs within agentic assistants invoke external tools and resources, such as databases and APIs, to solve problems faster and more effectively. The use cases for this design encompass customer support, dynamic information retrieval, and content editing.
• Planning: Assistants break down complicated jobs into manageable chunks and develop a clear plan for completing each step. A pattern like this is useful for applications like trip scheduling, patient diagnostics and treatment, and loan processing.
• Reason + Act (ReAct): LLM-powered agents first consider a problem and then act based on the results of their cognition. This thinking-and-doing approach makes the ReAct design pattern a perfect choice for data analysis, answering customer questions, trading bots, and other applications.

In terms of multi-agent setups, the entire network of AI assistants collaborates to fulfil assignments. The following multi-agent architectural patterns can be deployed on the OCI platform:

• Hierarchical/Vertical: The supervisor is in charge of other agents, delegating tasks to each member of the ecosystem in a hierarchical manner. Agents complete the assigned functions and report directly to their lead. This type of architecture is suitable in cases where multi-layer decision-making is important: for example, a complex IoT support system that requires advanced troubleshooting.
• Network/Horizontal: A team of agentic solutions forms a decentralized system, where they interact in a peer-to-peer, rather than hierarchical, fashion to exchange information and execute their portion of the job to reach a common objective. Applications for this design pattern include distributed decision-making, hyper-personalized customer support systems, and cybersecurity threat detection.
• Parallel: AI-driven assistants fulfill different assignments simultaneously and independently. The parallel blueprint is particularly beneficial for applications where efficiency is paramount, such as multi-modal data processing and real-time responsiveness in healthcare, trading, or industrial settings.
• Sequential: Agents take turns to refine the same task and achieve better outcomes without depending on each other’s outputs. A data processing pipeline with steps like information cleaning, analysis, and summarization is a practical scenario of using this architecture.
• Loop/Self-healing: Multiple agents repeat their assigned functions and self-improve until they achieve a desired result. An example of a loop agentic design pattern is a customer support platform powered by agentic AI, where each assistant focuses on improving a specific aspect of customer service: Agent 1 continuously refines responses to handle buyer queries, Agent 2 deals with returns and refunds, and Agent 3 persistently follows up on abandoned shopping carts.
• Router/Agentic RAG: One main agent routes tasks and incoming data to specialized retrieval agents for domain-specific information and knowledge acquisition. For instance, some assistants take information only from internal documents like emails and chats, while others retrieve data from external data sources, such as web searches.
• Aggregator/Synthesizer: AI agents work on sub-tasks independently, merging their outputs into a unified end result. An example of the aggregator architecture is e-commerce assistants, where Agent 1 evaluates reviews and identifies customer sentiment, Agent 2 tracks buyers’ purchase histories, and Agent 3 is responsible for inventory management. The agents then aggregate their results to achieve one shared goal—boosting customer satisfaction and sales.
• Branching/Conditional Processing: One primary agent assesses the given conditions, for example, agent availability, to decide which sub-agent should complete the job. A real-world application of branching is a customer support system where queries are directed to agents based on their accessibility.
• Ensemble/Voting/Consensus: Multiple assistants find various solutions to the same problem and then reach a consensus on the final, most accurate outcome using a majority voting method. This pattern occurs in AI diagnostic systems, where several agents assess medical images and then collectively agree on the diagnosis.
• Fallback/Error Handling: In case the main agent fails to complete the operation, a backup equivalent takes over. For instance, if Agent 1 hasn’t found relevant information in an internal knowledge base, Agent 2 will act as a back-up, performing web searches to successfully complete the task.
• Cascade/Progressive Refinement: Agents work step by step, adding incremental improvements to the results obtained by the previous assistant to refine the final outcome. An example use case of this design is a group of agents operating one by one to create a plan for a blog post, write the text, and make edits.

Why deploy agentic design patterns on OCI?

No matter which agentic architecture you’re planning to implement, the diverse services offered by the OCI platform will ensure a flawless process. Launching your AI agents on OCI with the help of an experienced company like EffectiveSoft allows you to achieve two goals simultaneously: eliminating potential deployment pitfalls and laying the groundwork for long-term business benefits. OCI AI development allows you to address common problems, such as high upfront infrastructure and maintenance expenses, increased latency, integration issues, incompatibility with existing systems, and security and compliance concerns. OCI also delivers significant business value, helping you create seamless digital user experiences, reduce operational costs, and support scalability based on evolving demands.