Design Pattern Evaluation

If you’re an AI engineer building multi-agent systems, this one’s for you. As AI applications evolve beyond single-task agents, we’re entering an era where multiple intelligent agents collaborate to solve complex, real-world problems. But success in multi-agent systems isn’t just about spinning up more agents, it’s about designing the right coordination architecture, deciding how agents talk to each other, split responsibilities, and come to shared decisions. Just like software engineers rely on design patterns, AI engineers can benefit from agent design patterns to build systems that are scalable, fault-tolerant, and easier to maintain. Here are 7 foundational patterns I believe every AI practitioner should understand: → 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Run agents independently on different subtasks. This increases speed and reduces bottlenecks, ideal for parallelized search, ensemble predictions, or document classification at scale. → 𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Chain agents so the output of one becomes the input of the next. Works well for multi-step reasoning, document workflows, or approval pipelines. → 𝗟𝗼𝗼𝗽 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Enable feedback between agents for iterative refinement. Think of use cases like model evaluation, coding agents testing each other, or closed-loop optimization. → 𝗥𝗼𝘂𝘁𝗲𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Use a central controller to direct tasks to the right agent(s) based on input. Helpful when agents have specialized roles (e.g., image vs. text processors) and dynamic routing is needed. → 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗼𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Merge outputs from multiple agents into a single result. Useful for ranking, voting, consensus-building, or when synthesizing diverse perspectives. → 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹) 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Allow all agents to communicate freely in a many-to-many fashion. Enables collaborative systems like swarm robotics or autonomous fleets. ✔️ Pros: Resilient and decentralized ⚠️ Cons: Can introduce redundancy and increase communication overhead → 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Structure agents in a supervisory tree. Higher-level agents delegate tasks and oversee execution. Useful for managing complexity in large agent teams. ✔️ Pros: Clear roles and top-down coordination ⚠️ Cons: Risk of bottlenecks or failure at the top node These patterns aren’t mutually exclusive. In fact, most robust systems combine multiple strategies. You might use a router to assign tasks, parallel execution to speed up processing, and a loop for refinement, all in the same system. Visual inspiration: Weaviate ------------ If you found this insightful, share this with your network Follow me (Aishwarya Srinivasan) for more AI insights, educational content, and data & career path.

Still building data platforms without clear design patterns? That’s where most pipelines break. This visual is a powerful reminder that data engineering isn’t about tools — it’s about patterns. Modern data systems scale not because of Spark, Snowflake, or Kafka… They scale because the right architectural patterns are applied at the right time. 🧩 What this image breaks down clearly 🔹 Ingestion Design Patterns • Batch ingestion for cost-efficient historical loads • Streaming ingestion for real-time use cases • CDC for low-latency, low-impact data movement 🔹 Storage Design Patterns • Data Lake for raw, flexible storage • Data Warehouse for curated analytics • Lakehouse for combining flexibility + performance 🔹 Transformation Patterns • ETL for schema-first, compliance-heavy systems • ELT for agile analytics and scalability • Incremental processing to avoid reprocessing everything 🔹 Orchestration & Workflow • DAG-based pipelines for complex dependencies • Event-driven pipelines for real-time architectures 🔹 Reliability & Fault Tolerance • Idempotent pipelines (safe re-runs) • Retry & dead-letter queues • Backfill patterns for safe historical reprocessing 🔹 Data Quality & Governance • Validation checks (nulls, ranges, constraints) • Schema evolution without breaking consumers • Data lineage for trust, debugging, and compliance 🔹 Serving & Consumption • Semantic layers to abstract complexity • API-based serving instead of direct table access 🔹 Performance & Scalability • Partitioning for faster queries • Caching to reduce compute and latency 🔹 Cost Optimization • Tiered storage for retention compliance • On-demand compute to avoid idle spend 🎯 Why this matters If you’re: • Designing a modern data platform • Scaling analytics for multiple teams • Migrating to cloud or lakehouse • Building real-time or AI-ready pipelines 👉 These patterns matter more than any single tool choice. 📌 Bookmark this. 📤 Share it with your data team. Question for you: Which of these patterns has saved you the most pain in production — and which one do teams usually ignore until it’s too late? #DataEngineering #DataArchitecture #AnalyticsEngineering #BigData #CloudData #ModernDataStack #Lakehouse #DataGovernance

Design patterns are terrific. When I first found out about design patterns, I was blown away. Factories, builders, and singletons. Everything. I studied them all and I used them everywhere, but I was particularly fond of the strategy pattern. If someone mentioned a future feature in passing I'd make sure our code was ready at all times. Found a piece of code to make more general? Time for the strategy pattern. "This keeps us agile!", I'd almost certainly have said in triumph as I demonstrated my flexible designs to my peers. But this soon started backfired. Hard. Despite good intentions, feature roadmaps change a lot. This meant my clever generalizations and abstractions had to get torn down frequently. Or worse, it was too hard to reverse them so I had to work around them. This slowed me down substantially. My clever design pattern efforts to move faster made me move slower. Because requirements change. They do that because we get wiser about what the customer wants. It's all in order. I still use design patterns though. The strategy pattern is still a favorite. But I'm far more selective and conservative about when to use it. I prefer to start with specific solutions over general ones. Letting them evolve as we add more cases. Then, if a pattern emerges and there's enough friction to warrant a refactor: I'll go for the design pattern. The lesson: Design patterns are useful, but premature design is counter-productive. It's more than okay to wait for a strong signal. (Note: this lesson goes beyond code-level optimizations. On the system level, premature design can be 100x worse because decisions aren't as easy to reverse). #softwareengineering

Part of testing or assessing an AI-based system is determining whether or not you the user, operator, designer, builder, or tester of that system can responsibly account for its behavior. Only people can be held accountable, AI cannot. To be held accountable, you must be able to describe and make decisions about the appropriateness of information about the system during operation and things the system does. There are design patterns which produce accountable AI systems. There are design patterns which make accountability impossible. The cartoon below offers two patterns that support accountability, and one which destroys. The key to accountability is for some component external to the AI to produce information about the state of the system and actions taken. One design pattern is for the AI extensions (or wrappers) to produce a log of information they presented to the AI, actions they took on behalf of the AI. Another design pattern is for code produced by the AI to generate a similar log, but also to stand as its own reviewable evidence of actions taken on behalf of the AI. An example might be an MCP server which receives and send email for an LLM. The MCP server could write a log of every message given, from whom, at what time, as well as ever message sent to whom at what point. This log allows a person to survey the behaviors without worrying that the LLM is producing fictional accounts of email sent and received. A similar example might be code written by the LLM to handle those same emails, which allows the additional advantage of human review, modification, acceptance, or rejection of the code before putting it into production. By contrast, when the extension produces no account of information, actions taken, decisions made, when the entire accounting of all of that relies entirely on the LLM, then the human is unable to responsibly account for the LLM activity. LLMs fabricate information, records of action, and fictionalize reasons and decision justifying those actions. Look for these patterns when you use AI based systems, or even when you casually use an LLM to solve problems ad hoc. It is easy to slide into non-accountable patterns and not realize it, but it is often easy to add means to account for what the LLM did or said by using external mechanisms to corroborate. #softwaretesting #softwaredevelopment #aitesting Try the AIGATE process for a methodical way of reviewing AI based systems for safe use. I am collecting case studies, contact me with your stories: https://lnkd.in/gkMqgGr4 You can find more of my articles and cartoons about testing in my book Drawn to Testing, available in Kindle and paperback format: https://lnkd.in/gB4NS4BS

𝗛𝗼𝘄 𝘁𝗼 𝘀𝗲𝗹𝗲𝗰𝘁 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻? Choosing the correct design pattern in software engineering is critical to practical problem-solving. This guide simplifies the process, helping you decide between patterns based on specific needs. It offers concise descriptions and valuable use cases for each pattern, making understanding and applying them in real-world scenarios easier. To select a pattern, we must first go through the problem identification. If the problem is related to:  🔸 Object Creation? → Creational Patterns  🔸 Object Assembly? → Structural Patterns  🔸 Object Interactions? → Behavioral Patterns So, let's dive in. 𝟭. 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀  🔹 Singleton: Use when a single instance of a class is needed. Some examples are logging and database connections.   🔹 Factory Method: Decouple object creation from usage. For example, you create different types of database connections based on configuration.  🔹 Abstract Factory: Create families of related objects. For example, I build parsers for different file formats (e.g., JSON, XML, CSV).  🔹 Builder: Constructing complex objects step by step. For example, if you need to create a complex domain object.  🔹 Prototype: Creating duplicate objects and reusing cached objects to reduce database calls. 𝟮. 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀  🔹 Adapter: Make incompatible interfaces compatible. For example, it integrates a new logging library into an existing system that expects a different interface.  🔹 Composite: Represent part-whole hierarchies. For example, graphic objects in a drawing application can be grouped and treated uniformly  🔹 Proxy: Control access to objects. For example, lazy loading of a high-resolution image in a web application.  🔹 Decorator: Dynamically add/remove behavior. For example, we are implementing compression or encryption on top of file streams.  🔹 Bridge: Decouple abstraction from implementation. For example, I am separating platform-specific code from core logic. 𝟯. 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀  🔹 Strategy: Define a family of algorithms. These algorithms allow users to choose different sorting or compression algorithms.  🔹 Observer: Maintain a consistent state by being notified of changes and, for example, notifying subscribers of events in a messaging system.  🔹 Command: Encapsulate a request as an object. For example, I implement undo/redo functionality in text or image editor.  🔹 State: Encapsulate state-specific behavior. For example, we are handling different states of a user interface element (e.g., enabled, disabled, selected).  🔹 Template Method: Define the skeleton of an algorithm in operation, deferring some steps to subclasses and implementing a base class for unit testing with customizable setup and teardown steps. Ultimately, we came up with the pattern we needed for our problem. #technology #softwareengineering #programming #techworldwithmilan #softwaredesign

✨ 𝟳 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 Design and build robust Multi-agent systems with these: 𝟭. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Multiple agents work simultaneously on different parts of a task. - Strengths: Boosts efficiency through parallel processing. - Challenges: Coordination can get tricky with overlapping efforts. - Use Case: Think real-time data analysis across multiple sources. 𝟮. 𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Tasks flow linearly, with one agent’s output feeding into the next. - Strengths: Simple and structured—perfect for multi-step workflows. - Challenges: Bottlenecks if one agent lags. - Use Case: Multi-step approvals or phased content generation. 𝟯. 𝗟𝗼𝗼𝗽 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Agents iterate in cycles, refining outputs based on feedback. - Strengths: Ideal for continuous improvement. - Challenges: Risk of over-iteration without clear stopping criteria. - Use Case: Code writing and testing loops for precision. 𝟰. 𝗥𝗼𝘂𝘁𝗲𝗿 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 A central router assigns tasks to the right agent(s) based on input. - Strengths: Streamlines task delegation dynamically. - Challenges: Router becomes a single point of failure. - Use Case: Customer support triage with specialized agents. 𝟱. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 (𝗦𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘇𝗲𝗿) 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Agents contribute outputs, synthesized into a final result by an aggregator. - Strengths: Combines diverse expertise into cohesive outcomes. - Challenges: Synthesis can be complex if outputs conflict. - Use Case: Compiling research from multiple agents into a report. 𝟲. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹) 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Agents communicate directly in a decentralized, many-to-many setup. - Strengths: Resilient—system persists even if some agents fail. - Challenges: Communication overhead can slow things down. - Use Case: Collaborative problem-solving in dynamic environments. 𝟳. 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 (𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹) 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 Agents are organized in a tree, with supervisors managing subordinates. - Strengths: Clear roles and streamlined decision-making. - Challenges: Top-level failures can cascade. - Use Case: Large-scale systems like corporate AI workflows. These patterns, borrowed from software engineering, are flexible and adaptable. These patterns aren't just theoretical—they're practical tools for building robust AI systems. Whether you're orchestrating a retrieval-augmented generation pipeline or managing specialized agents, these patterns are your blueprint to success. What do you think? ---- 🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX Repost if this helped you see the shift ♻️

🚀 Multi-Agent Design Patterns Multi-agent systems are becoming the backbone of modern AI applications. Instead of relying on a single large model, work is distributed across specialized agents with clear coordination rules. This design improves scalability, reliability, and reasoning quality. 👉 Core multi-agent coordination architectures 1. Sequential pattern Best suited for deterministic workflows. Each agent completes a step and passes structured output to the next agent. Example: Document ingestion agent → extraction agent → validation agent → final report. 2. Parallel pattern Multiple agents run simultaneously on the same query and results are merged. This reduces latency and improves coverage. Example: An enterprise search system where separate agents analyze policy, legal, and technical documents in parallel, followed by a summarization agent. 3. Hierarchical pattern A manager agent decomposes the task and delegates work to child agents, then aggregates results. Example: A planning agent assigns subtasks to forecasting, risk, and cost optimization agents before producing a unified recommendation. 4. Generator and Critic pattern One agent generates output while another evaluates quality, correctness, or compliance. Feedback loops continue until criteria are met. Example: A code generation agent paired with a critic agent that checks security issues and logic errors before approval. 5. Human in the loop pattern AI generates a draft, but critical decisions require human approval before execution. Example: An AI billing agent drafts invoices, a human reviewer approves them, and only then is the transaction processed. 6. Composite patterns Production systems combine multiple patterns for robust and reliable reasoning. Example: A Meta Agent orchestrates parallel research agents, applies generator critic refinement, and iterates until a high confidence answer is produced. 💡 Why this matters Multi-agent design enables controllable AI systems with stronger reasoning, reduced hallucinations, and clear auditability. This is the direction enterprise AI systems are evolving toward. ➕ Follow Shyam Sundar D. for practical learning on Data Science, AI, ML, and Agentic AI 📩 Save this post for future reference ♻ Repost to help others learn and grow in AI #AgenticAI #MultiAgentSystems #AIArchitecture #GenerativeAI #RAG #LLMOps #DataScience #MachineLearning