Jahaziel Rodriguez Vegas’ Post

To leaders in Engineering and Content Strategy. The market is obsessed with execution speed. Tools like Claude Code allow anyone to build applications and connect databases in minutes. The industry praises this velocity. However, speed without visual governance creates a massive liability. When autonomous agents orchestrate workflows without strict user experience constraints, they inject severe technical debt. Comprehension debt accumulates. Human operators completely lose the ability to trust the system. This disaster happens because protocols like the Model Context Protocol connect models directly to enterprise data. These protocols reverse traditional control patterns. An agent can execute a destructive database action in milliseconds. Real innovation is not about generating code faster. Real innovation is designing the Trust Boundaries that make code safe to operate. We urgently need an Experience Contract dictated by a deterministic Design System. This requires implementing standardized design components that visually expose the certainty of the artificial intelligence. The interface must display this confidence before any critical action is executed. If the system lacks absolute confidence, the interface must enforce a strict, high friction confirmation step. If the confidence is high, a subtle inline approval component is sufficient. The Design System must serve as the structural sandbox that keeps a human in the loop. Are you building systems of speed, or are you building systems of trust? Stop optimizing for fast compilation alone. Start designing the visual guardrails that protect your human operators. #AgenticAI #DesignSystems #UXEngineering #ContentStrategy #InformationArchitecture #B2BSaaS

Claude Teams Plan Hit Its Limit. All 24 Users Got Blocked. Deployed Claude Teams at my company as part of a broader AI adoption evaluation, mix of Standard and Max seats. Six weeks in, I tried out the org-level usage credit pool due to Claude Fable running out of my usage limit (I am on the Max Plan). Claude didn't check the Excel file I loaded into it for obsolete names before running an extensive build. I turned on the usage credits to complete the task proceeded to run out again. Everyone stopped working. No fallback to seat-level limits. No per-user controls. Just a wall. The product design assumes you'll set a spend cap and stay under it. When you don't, Anthropic blocks the entire organization, regardless of seat tier. Teams gives you the illusion of overage flexibility but implements a shared organizational pool with a hard stop. It's enterprise-grade billing discipline wrapped in consumer-grade product friction. I disabled usage credits and reverted to seat-based limits. Standard seats get their base allowance, Max seats get theirs. Reset on schedule. It works, but it exposed a real gap: Anthropic optimized this for billing predictability and risk control, not for how teams actually adopt AI tools. Enterprise gets per-seat-tier pooling and granular controls. Teams doesn't. The models are excellent. The go-to-market needs maturity work. If you're evaluating Anthropic for your org, understand the usage credit mechanics before rollout—or you'll spend your first two weeks explaining to your users why they can't access the tool you just enabled. Anthropic thumbs down at this point of the pilot.

You cannot scale a global enterprise refresh using manual asset pipelines. The design debt is due and you need global alignment immediately, across all teams. Recently, during a massive 36+ multi-brand transformation, I realized the bottleneck was the missing infrastructure. The sheer volume of generating foundational visual assets for 100+ products was heavily reliant on manual processes, creating a mountain of dev debt and fragmented UI components. It was time to scale to 8 markets. Mid-spreadsheet, I shifted into systems architecture mode and engineered a custom asset taxonomy web app powered by Claude. By integrating Claude into our workflow, we fundamentally changed how we handled our production pipeline: Automated Taxonomy: We used the tool to automate end-to-end image naming, PDP component strings, and complex database mapping across 54+ unique templates. Eliminated Dev Debt: We replaced tedious, error-prone manual data entry with a scalable, dynamic data framework. De-Risked Delivery: It guaranteed strict product accuracy and frictionless PIM alignment with our Product Managers. Introducing a new AI tool mid-refresh has been the key to our acceleration and clean handoff. AI is no longer just for brainstorming; when integrated correctly, it is the infrastructure that allows enterprise design to scale safely and efficiently. If your cross-functional teams are still manually managing asset pipelines, it might be time to look at your systems architecture. #DesignSystems #DesignEngineering #AIWorkflows #SystemsArchitecture #ProductDesign #GenerativeAI #DesignOps

This week reminded me why building enterprise software is so different from building a prototype. We're currently working on the next edition of our Can Nova Build This? series, using a real Product Owner brief from the financial services industry. What has stood out isn't the UI. It's the conversations. Every requirement raises new questions. Every workflow uncovers another business rule. Every decision shapes how the product will actually be used. AI is making it easier to build software. Understanding the business problem is still where the real value is created. Looking forward to sharing what we've built very soon. #EnterpriseAI #ProductManagement #SoftwareDelivery #Innovation #Nova

I used to think the clever part was building the tool. Now I think the harder part is finding the tiny handoff that keeps breaking. The place where everyone thinks the system is working because there is technically a process. A Notion board. A shared folder. A project thread. A checklist. A few reminders. But then you ask one simple question and everything gets a bit awkward. What is still missing? Who needs to approve this? Which version is final? Did the client ask for changes or just leave a comment? Has anyone updated the internal workspace? That is where I’ve become more interested in workflow design. Not big complicated systems. Not software for the sake of software. Just focused tools that remove one painful bottleneck properly. That’s what I’m trying to do with Chasepad, and it’s also the kind of work I’m building around more generally: SaaS products, Notion systems, AI agents and client-facing workflows. The common thread is simple. Make the next step clearer. Because a good system should not just store information. It should reduce the amount of chasing, guessing and rechecking a team has to do. #WorkflowDesign #SaaSFounder #NotionSystems #AgencyOps #ClientWork

AI agent token bloat and choice paralysis usually trace back to a single architecture flaw: loading every single tool into the system upfront and calling it a day. When we dump 50 tools into an agent's context window, reasoning accuracy drops as the API bill skyrockets. Its a lose-lose strategy. To scale complex agentic workflows cleanly, you have to establish strict structural work boundaries. Today, us developers solve this by splitting logic into separate Agent Personas, delegating tasks between each other. I propose at least one new pattern that's been working for me at home, "Progressive AX". - Progressive AX (In-Thread Continuity): Treating tools like application pages. The orchestration harness dynamically mounts and unmounts specific tool schemas as the agent shifts states, preserving memory while keeping the prompt lean. - SubAgent Delegation (Context Isolation): Treating tasks like hiring a specialized coworker. The supervisor passes a clean payload to an isolated worker instance, completely hiding noisy intermediate trial-and-error loops from the main history thread. I've mapped out the key differences between these two patterns: the technical lifecycles, structural risks, and engineering gains for both routing frameworks in my latest architecture guide. Link to the full gist and implementation guidance below! 👇 https://lnkd.in/gKd_TdCV How are you currently managing tool schemas and token degradation in multi-step agent workflows? What are some other patterns you've exploring? #AIEngineering #SubAgents #AgenticAI #SoftwareArchitecture #ProgressiveDisclosure #UX #GenAI #AgentExperience #AX

Buying another platform isn’t going to fix what discipline hasn’t solved… Adding tools feels productive. Integration is what actually moves the needle. From my perspective, a lot of small and mid-sized businesses don’t have an issue with tools. It comes down to integration. I look at our own tech stack, and we have partners that are shipping updates every single month. AI features, automation layers, workflow improvements. The reality is we haven’t adopted half of what’s already available to us. You go through one demo, sign the contract, and then use the platform for years without ever sitting back down with that partner. Meanwhile, they’ve pumped millions, sometimes billions, into innovation that could improve margin or efficiency if someone actually reviewed it with intention. Discipline looks like structured reviews. Start having periodic spot checks where you ask: • What are we underutilizing? • Where are we leaving savings on the table? • What capabilities are we paying for but not deploying? Before adding another logo to your stack, review what you already have.

Anthropic just expanded the Claude build surface faster than most people realize. At their Code with Claude developer event, they shipped 5 features: Dreaming (background async processing), Outcomes (structured agent goals), multi-agent orchestration, Claude Finance with 10 pre-built financial workflow agents, and Add-ins (agent plugins for co-work and Claude Code). Our take: this is not an incremental update. These primitives change what you can build and how fast. The multi-agent orchestration layer means you can now coordinate fleets of specialized agents without managing the orchestration yourself. The Finance agents - pitch builder, market researcher, month-end closer - give you proof-of-concept vertical AI that you can fork and deploy today. The Add-ins system is quietly the biggest one. Any agent you build can now surface inside Claude.ai workflows as a plugin. That is distribution built in, not just capability. For builders, the takeaway is simple: the surface area for what you can ship just got bigger. The question is what you build on top of it. At Agent Architects, we are already mapping these primitives to real automation use cases. If you are building with Claude or thinking about it, now is the time to map out where multi-agent and Add-ins fit your stack. Stop doing it manually. Join 1,000+ founders automating their business - theagentarchitects.com #AIAgents #ClaudeCode #AgentArchitects #BuildWithAI

Building the agent is the easy part. Choosing the right architecture is where most teams get stuck. I put the 8 most common AI agent architectures into one visual, based on how they actually run in production workflows, not theory. A quick guide: 1️⃣ 𝐒𝐢𝐧𝐠𝐥𝐞 𝐀𝐠𝐞𝐧𝐭 + 𝐓𝐨𝐨𝐥𝐬 One agent, one model, a handful of tools (Gmail, Calendar, Contacts). The right starting point for most use cases. 2️⃣ 𝐒𝐢𝐧𝐠𝐥𝐞 𝐀𝐠𝐞𝐧𝐭 + 𝐌𝐂𝐏 𝐒𝐞𝐫𝐯𝐞𝐫𝐬 + 𝐓𝐨𝐨𝐥𝐬 Same shape, but tools arrive through MCP. The agent plugs into Atlassian, Calendar, or anything else without custom glue code. 3️⃣ 𝐒𝐢𝐧𝐠𝐥𝐞 𝐀𝐠𝐞𝐧𝐭 + 𝐓𝐨𝐨𝐥𝐬 + 𝐑𝐨𝐮𝐭𝐞𝐫 An If node routes the agent's output to different endpoints. Small addition, big shift: this is where a workflow starts becoming a system. 4️⃣ 𝐒𝐢𝐧𝐠𝐥𝐞 𝐀𝐠𝐞𝐧𝐭 + 𝐇𝐮𝐦𝐚𝐧 𝐢𝐧 𝐭𝐡𝐞 𝐋𝐨𝐨𝐩 A Slack approval step sits between the agent and the outcome. The agent does the grunt work. You make the calls that matter. 5️⃣ 𝐒𝐢𝐧𝐠𝐥𝐞 𝐀𝐠𝐞𝐧𝐭 + 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐀𝐠𝐞𝐧𝐭 𝐂𝐚𝐥𝐥𝐬 The agent treats other agents as tools and decides when to delegate. 6️⃣ 𝐒𝐞𝐪𝐮𝐞𝐧𝐭𝐢𝐚𝐥 𝐀𝐠𝐞𝐧𝐭𝐬 Two agents in a chain, each with its own model, memory, and tools. Clean handoffs, clear responsibilities. 7️⃣ 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐲 + 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥 𝐀𝐠𝐞𝐧𝐭𝐬 + 𝐒𝐡𝐚𝐫𝐞𝐝 𝐓𝐨𝐨𝐥𝐬 A switch fans work out to parallel agents, a merge brings the results back. Shared tools keep everything consistent. 8️⃣ 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐲 + 𝐕𝐞𝐜𝐭𝐨𝐫 𝐒𝐭𝐨𝐫𝐞 Same pattern, plus embeddings and a vector store. Now agents work with knowledge, not just tools. The pattern behind the patterns: Every step up adds control, not intelligence. Routers, approvals, merges, memory. That is the "95% software engineering" part of building agents. Start with 1. Add structure only when the workflow demands it. Save the visual for your next build. Curious which of these you are actually running today? Most teams I talk to are somewhere between 1 and 4. _________ Learn how to build AI Agent Architectures in Practice here: https://lnkd.in/gmstxGSv ____________ #AIAgents #AgenticAI #GenerativeAI #AIEngineering

❓𝗧𝗵𝗿𝗲𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘄𝗼𝗿𝘁𝗵 𝗮𝗻𝘀𝘄𝗲𝗿𝗶𝗻𝗴 𝗯𝗲𝗳𝗼𝗿𝗲 𝗤𝟯. The next few weeks are quieter for most teams. Inboxes thin out, the calendar clears, and there's actually time to think about something other than the next deadline. If you're working on or near a modular product platform, three questions are worth sitting with before everyone returns and Q3 planning kicks in: ◼️ What percentage of orders this year required engineering intervention outside the platform? If you don't know the number, that's the answer. ◼️ Which decisions about the platform got deferred this year because "we'll figure it out later"? Those decisions are now sitting in someone's backlog, quietly costing you. ◼️ Where does the platform's configuration logic actually live? In the system, in spreadsheets, in three people's heads? The honest answer tells you how ready you are for whatever your AI strategy turns into. None of these require a workshop or a consultant. They require an hour of honest thinking. The answers tend to make the second half of the year clearer than the first. #Manufacturing #ProductArchitecture #Modularization #Q3Planning