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Summary
Modular design approaches involve breaking down complex systems into smaller, independent modules that can be developed, tested, and refined separately. This method makes it easier to manage, scale, and adapt technology projects—whether in AI, hardware, or software—by promoting clear boundaries and collaboration between components.
Organize components: Separate different functions and responsibilities into distinct modules so you can easily track, test, and update each part without affecting the whole.
Enable rapid changes: Structure your project so modules can be swapped, improved, or expanded as needs evolve, supporting experimentation and continuous improvement.
Improve troubleshooting: Build with modularity so you can isolate and fix problems faster, making debugging and maintenance simpler for everyone involved.
Working with multiple LLM providers, prompt engineering, and complex data flows requires thoughtful organization. A proper structure helps teams:
- Maintain clean separation between configuration and code
- Implement consistent error handling and rate limiting
- Enable rapid experimentation while preserving reproducibility
- Facilitate collaboration across ML engineers and developers
The modular approach shown here separates model clients, prompt engineering, utils, and handlers while maintaining a coherent flow. This organization has saved many people countless hours in debugging and onboarding.
Key Components That Drive Success
Beyond folders, the real innovation lies in how components interact:
- Centralized configuration through YAML
- Dedicated prompt engineering module with templating and few-shot capabilities
- Properly sandboxed model clients with standardized interfaces
- Comprehensive caching, logging, and rate limiting
Whether you're building RAG applications, fine-tuning foundation models, or creating agent-based systems, this structure provides a solid foundation to build upon.
What project structure approaches have you found effective for your generative AI projects? I'd love to hear your experiences.
AI Workflow vs. AI Agent-Based Systems
I set up a side-by-side comparison of two distinct approaches:
Classic AI Workflow:
- Linear Progression: This approach starts with a query that passes through an orchestrator, which then triggers sequential tasks like multiple LLM calls and function calls (e.g., Search APIs and Vector Search).
- Centralized Processing: All operations funnel toward a synthesizer that combines the results into a single output, offering a predictable and straightforward processing pipeline.
- Deterministic Behavior: With clear, step-by-step stages, this design is easier to manage and debug, making it ideal for scenarios where consistency and transparency are key.
Agent-Based System:
- Modular Design: Instead of a single processing pipeline, a meta-agent distributes tasks among several specialized sub-agents. Each sub-agent focuses on a specific aspect of the query.
- Decentralized Execution: The outputs from these sub-agents are aggregated, and a feedback loop sends information back to the meta-agent, fostering continuous improvement and refinement.
- Enhanced Flexibility: This architecture is better suited for complex or evolving problems, as it allows for parallel processing and iterative adjustments, potentially leading to more nuanced results.
Why It Matters: Choosing between a classic workflow and an agent-based system depends on your project's needs. If you value a clear, linear process with easier troubleshooting, the classic approach might be the way to go. However, if your application demands flexibility, scalability, and iterative refinement, a modular, agent-based architecture could offer significant advantages.
Over to you: Which design do you think is better suited for today's AI challenges?
We recently completed 88 modular units at 1888 in Oakland, which is now permanent shelter for the homeless, being run by HCEB.
We’ve certainly had our ups and downs on modular projects in the past, so I wanted to share a few of the critical things we’ve learned from them.
To start, modular construction can offer time savings that equal dollars (and most agree that if savings are to be achieved, this is where it happens) but it requires careful planning upfront to avoid costly delays later.
- Early Coordination is Critical
Cost savings come from speed resulting in reduced carrying costs relies on backend efficiency, not upfront expenses. Expect higher early costs for design and coordination to prevent site issues.
- Keep it Simple
This may be generally true, but particularly so with modular design. More corners (in plan or section) increase costs.
- Define Key Connection Points
MEP, structural tie-ins, exterior skin, and roof must align. Carefully consider these connections to avoid expensive rework.
- Establish a Responsibility Matrix
Clearly define who handles factory vs. site work (MEP connections, finishes, structural tie-ins). Avoid scope gaps between factory, GC, and set contractor.
- Plan for Storage and Logistics
Timing is never perfect—modules may need storage before installation. Ensure proper staging, crane access, and transport coordination in advance.
- Quality Assurance Beyond State Inspections
State inspections confirm code compliance, not necessarily construction quality. Implement independent QA checks at factories and site for alignment, waterproofing, and tolerances.
- Protect Set Units from Weather
Modules are exposed between placement and final enclosure. Plan for temporary protection—tarps, shrink wrap, or other covers—in case of unexpected or inclement weather.
Final Thought
Modular can be fast and save cost —but only if planned right. Upfront work prevents delays, misalignment, and costly fixes.
If you made it this far you must be a true modular nerd (like me). What challenges have you faced with modular projects?
Photos by Bénédicte Lassalle
Single prototypes tell you nothing about system reliability.
Modularity is the secret key you're missing.
When we built the multi-function demonstrator for Hyundai Cradle, we created a series of modular prototypes.
Each targeted at validating specific performance vectors.
→ Thermal modules tested for uniformity and delta-T across surfaces
→ Touch and switch modules evaluated for actuation force versus signal-to-noise ratio
→ Pressure sensing modules designed to maintain accuracy under cyclic compression and lateral shear
Key variables we isolated included:
→ Material stack-up compression profiles during environmental cycling
→ UV adhesive bond stability across operational temperature bands (-40°C to +85°C)
→ Electrical resistance drift under flexural fatigue testing (bend radius <5mm, 10,000+ cycles)
By modularizing early, we could:
→ Identify failure modes before scaling
→ Fine-tune adhesives, conductors, and substrates independently
→ Model manufacturing tolerances with real data, not assumptions
In hardware, scalable design isn’t about the first build.
It’s about how you architect your prototyping process.
Design products, process, plants and infrastructure are shifting from projects to products.
I see one move that cuts through disconnected people, processes and data. Productize your design work. Treat repeatable scope as configurable modules with defined interfaces, a single source of truth, and clear change rules. Do that, and collaboration stops being heroics, interoperability pain eases, and re-use beats re-invention.
The market signals are hard to ignore. Modular programs have shown 20–50% faster timelines. Capital projects still overshoot budgets by about 79% and slip by months or years. Around 41% of the US construction workforce is expected to retire by 2031, while buildings account for 39% of energy-related emissions and modular methods can cut site waste by 70–90%. Cloud-based collaboration and digital twins are closing the loop between design, fabrication and assembly so teams work from one living model, not stale documents.
What does this look like in practice for E&U? Build a standard module catalog for common plant systems and site packages. Define interface contracts so teams can work in parallel without constant meetings. Keep one connected model as the system of record, with lightweight change control that ties requirements, design, and field feedback. Start with one asset class, prove cycle time and quality, then scale.
As AI systems evolve from models to agents, one thing is clear monolithic design won’t scale, but modular architecture will. I spent some time last week exploring how this shift is reshaping the future of intelligent systems.
In the agentic era, modular architectures are redefining how intelligence is built, orchestrated, and governed. Each module Reasoning, Memory, Perception, Orchestration, and Governance plays a distinct role, working together like a digital symphony.
This architectural shift matters because it delivers Scalability, Resilience, and Transparency. Each layer from reasoning to policy control can be audited, explained, and improved independently, ensuring responsible and adaptable AI systems.
We’ve already seen this modular mindset succeed across applications from personalization engines that dynamically recompose user journeys to commerce agents that reason, act, and adapt in real time.
As AI matures from static models to autonomous agents, modularity isn’t just engineering elegance, it’s the foundation for trustworthy, scalable, and future-ready intelligence.
How do you see modular architecture shaping the future of AI systems?
#Agents#Architecture#modular#AI#GenAI#Personalization#Leadership#FutureofAI#reviewswithsrujana
I’ve previously shared that true modularity is nearly impossible to achieve when working across a wide range of projects.
That said, I have found things which are transferable. So what actually carries over?
Here are a few "blocks" I’ve found to be consistently reusable across very different systems:
🔁 Recipe Handling
Many machines require recipes. While the structure may change, the core tends to remain the same: loading, storing, and switching between sets of values.
🧼 Input Validation
- Debouncing: Ensuring an input remains active for a minimum duration before accepting it as valid.
- Clamping: Constraining incoming values to stay within defined boundaries.
⚠️ Output Validation
- Auto-resetting outputs that shouldn't remain on, especially in the case of communication failure or abnormal state.
- Preventing "stuck-on" conditions for outputs that should only pulse or toggle.
📏 Analog Scaling
- Normalizing to a standard range.
- Clamping values to safe operating limits.
- Embedding alarms or fault thresholds for over/under-range conditions.
No matter what I do, these building blocks often find a place.
#IndustrialAutomation#PLCProgramming#ControlSystems#ModularDesign#EngineeringBestPractices#AutomationEngineering#ReusableCode#MachineControl#SCADA#ProcessAutomation#IndustrialControls#StructuredText#CodeStandards#AutomationLogic#EngineeringInsights#plc#ladderlogic#functionblocks#aois#ladder#SystemIntegrator
If you are building AI agents or learning about them, then you should keep these best practices in mind 👇
Building agentic systems isn’t just about chaining prompts anymore, it’s about designing robust, interpretable, and production-grade systems that interact with tools, humans, and other agents in complex environments.
Here are 10 essential design principles you need to know:
➡️ Modular Architectures
Separate planning, reasoning, perception, and actuation. This makes your agents more interpretable and easier to debug. Think planner-executor separation in LangGraph or CogAgent-style designs.
➡️ Tool-Use APIs via MCP or Open Function Calling
Adopt the Model Context Protocol (MCP) or OpenAI’s Function Calling to interface safely with external tools. These standard interfaces provide strong typing, parameter validation, and consistent execution behavior.
➡️ Long-Term & Working Memory
Memory is non-optional for non-trivial agents. Use hybrid memory stacks, vector search tools like MemGPT or Marqo for retrieval, combined with structured memory systems like LlamaIndex agents for factual consistency.
➡️ Reflection & Self-Critique Loops
Implement agent self-evaluation using ReAct, Reflexion, or emerging techniques like Voyager-style curriculum refinement. Reflection improves reasoning and helps correct hallucinated chains of thought.
➡️ Planning with Hierarchies
Use hierarchical planning: a high-level planner for task decomposition and a low-level executor to interact with tools. This improves reusability and modularity, especially in multi-step or multi-modal workflows.
➡️ Multi-Agent Collaboration
Use protocols like AutoGen, A2A, or ChatDev to support agent-to-agent negotiation, subtask allocation, and cooperative planning. This is foundational for open-ended workflows and enterprise-scale orchestration.
➡️ Simulation + Eval Harnesses
Always test in simulation. Use benchmarks like ToolBench, SWE-agent, or AgentBoard to validate agent performance before production. This minimizes surprises and surfaces regressions early.
➡️ Safety & Alignment Layers
Don’t ship agents without guardrails. Use tools like Llama Guard v4, Prompt Shield, and role-based access controls. Add structured rate-limiting to prevent overuse or sensitive tool invocation.
➡️ Cost-Aware Agent Execution
Implement token budgeting, step count tracking, and execution metrics. Especially in multi-agent settings, costs can grow exponentially if unbounded.
➡️ Human-in-the-Loop Orchestration
Always have an escalation path. Add override triggers, fallback LLMs, or route to human-in-the-loop for edge cases and critical decision points. This protects quality and trust.
PS: If you are interested to learn more about AI Agents and MCP, join the hands-on workshop, I am hosting on 31st May: https://lnkd.in/dWyiN89z
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