Choosing the right AI Agent framework: experimental or production-ready?

Yes, AI can move fast—but speed isn’t the breakthrough. Context is. The truth is, building with AI isn’t just about writing clever prompts. It’s about designing context the same way we’ve always had to design software systems. Think about it like software development in the old days: --> You still needed architecture. --> You still needed “include files” and libraries. --> You still needed to define the rules before the code made sense. AI is no different. In fact, because it’s faster and smarter, the stakes are higher. If we don’t provide structured context, the output unravels just as quickly as it’s produced. That’s why I’m excited about the idea of Context Engineering—it’s not a gimmick, it’s the new operating system for complex AI work. At the end of the day, context really is king.

AI frameworks promise fast agents without understanding how they work. Here's the catch... 🤔 AI frameworks today sell a tempting promise: "Build agents fast, no deep knowledge required!" Great for prototypes and impressing stakeholders. But here's what happens next: Production accuracy requirements hit. Complex functionality needs emerge. Access control becomes critical. Suddenly, the framework starts working against you instead of with you. The reality? There's still a place for frameworks - especially for smaller teams or when MVP speed is everything. But for complex cases, building from scratch often wins. Just wrote a full breakdown of the options teams should consider when building agents - covering when frameworks make sense, when they don't, and everything in between. What's been your experience - frameworks helping or hurting in production?

What we’ve learned building real-world AI products After working across multiple enterprise AI deployments, one insight has become painfully clear: Specificity = Trust. Specific prompts → specific responses → specific user value. This is not a “fine-tuning” problem. It’s a product design problem. The onus is on product teams to shape AI outputs that feel tailored, useful, and human. ⸻ For builders and product leaders: 💡 Start here: • Build for narrow workflows, not broad topics. • Inject relevant context at every step—user role, metadata, previous actions. • Write and test prompts like you write UI copy: intentionally, repeatedly. • Don’t let your “default” be “generic.” ⸻ AI works. But only when we stop treating it like magic—and start treating it like product. ⸻ Would love to hear from others building real AI systems: What’s your favorite way to inject specificity?

Beyond Proof of Concept: Making Generative AI Solutions Production-Ready  Many generative AI projects never make it past the proof-of-concept (POC) stage. The gap between a slick demo and a production-ready solution is often massively underestimated. After working on multiple GenAI initiatives, here are a few tips I’ve learned about designing AI solutions that actually deliver in production: 1️⃣ Start with a real business problem Don’t chase “cool demos.” Anchor your solution to a business-critical problem tied to measurable outcomes (efficiency, accuracy, revenue). 2️⃣ Design for validation from the start GenAI is non-deterministic. Always give business users a way to check the output — whether through citations, context, or confidence scores. 3️⃣ Involve business users early Real feedback > lab assumptions. Get business users into the loop before launch and treat their input as fuel for product evolution — not just bug reports. 4️⃣ Apply the 80/20 rule Prioritize the 20% of features that drive 80% of the value. A simple, reliable solution beats a fragile “feature-complete” one every time. 5️⃣ Build for monitoring + iteration Launch isn’t the finish line. Data shifts, user behavior evolves, and edge cases appear. Bake in monitoring, feedback loops, and retraining processes from day one. 6️⃣ Have responsive AI governance ready Policies and guardrails shouldn’t just exist on paper — they need to adapt as models, regulations, and business use cases evolve. Governance must be agile enough to support innovation and protect against risk. 👉 The key: design for scalability, reliability, and usability from the very beginning. What strategies have you found most effective in taking GenAI from POC to production? #GenerativeAI #AIinProduction #AIProductDevelopment #POCtoProduction

I recently attended a 2-day workshop on Generative AI, and as a beginner, it was eye-opening to explore the incredible breadth of use cases already available. Here are 3 key takeaways for me: 1. Structure your prompts: • Use Role, Context, Task, and Constraints to bring clarity • Use # to add headers and **...** to make important elements bold in the prompt • This approach helps reduce misunderstandings, especially when the prompt is long 2. Create agents for routine work: Why re-prompt daily? Tools like Gemini allow you to create free "Gems" to automate recurring tasks such as research and drafting 3. Pair AI with Automation:The real magic happens when AI collaborates with no-code tools: Idea → AI draft → human review → auto-publish across channels ⚠️ Note that most tools have associated costs I was truly amazed by the possibilities—even without coding! However, I also recognize that there are challenges to overcome 👉 As a beginner, I find the breadth of use cases exciting! What about you? Do you feel inspired by the possibilities, or are the challenges more on your mind? #GenAI #Automation #Futureofwork

This is an excellent interview with Asha Sharma on the shift from org charts to work charts. Layer137 believes the future of sales isn’t about replacing people with AI it’s about making people more effective. A “work chart” helps you see where AI can have the biggest impact in your sales cycle. Here’s how: 1. List the work, not the roles. Write down tasks like “qualify leads” or “prep for meetings.” 2. Map the workflow. Connect those tasks into a simple flow of how sales really happens. 3. Spot the friction. Find the slow, repetitive, or error-prone steps. 4. Overlay AI agents. Use AI to: Automate, Enrich, and Route work to the right person at the right time Listen to the full conversation here: https://lnkd.in/eAGJ7dxb #FutureOfWork #AI #Sales #WorkChart #Layer137

Thank you TechRadar for sharing my thoughts on the benefits of AI that go beyond time-savings and into decision-making. I've experienced this in my own work with AI tools, and we're also seeing it at AlphaSense where our customers use our GenAI capabilities not only to work faster but also to make more informed, strategic decisions. In today’s world, speed matters. But GenAI is more than a tool for saving time. It’s also reshaping how organizations make decisions and innovate. Read the full article here: https://lnkd.in/e3qcQD8t

🤖 Enterprise-Scale AI Agents: More Than Just an LLM 🚀 To build production-ready AI agents, we need more than a single model. We need a full ecosystem 🌐—and that’s exactly what the AI Agent System Blueprint shows. 🔹 1. Input / Output – Flexible User Interaction 🎤📄🖼️🎥 Agents today go beyond text. They process documents, images, audio, video so users can interact naturally and contextually. 🔹 2. Orchestration – The Nervous System 🧠⚡ Frameworks like LangGraph, Guardrails, Google ADK manage: 🌀 Context handling 📡 Streaming & tracing 🚀 Deployment & evaluation 🛡️ Guardrails for safety & compliance ➡️ Without orchestration: fragile demos ❌ ➡️ With orchestration: scalable, reliable systems ✅ 🔹 3. Data & Tools – Context is Power 🔑📚 Agents get smarter when connected to: 📊 Vector & semantic DBs 🏢 Internal knowledge bases 🔌 APIs (Stripe, Slack, Brave, etc.) This keeps decisions grounded in enterprise context, not hallucinations. 🔹 4. Reasoning – The Brains of the System 🧩🧠 Different models work together: 💬 LLMs (Gemini Flash, GPT-4o, DeepSeek R1) ⚡ SLMs (Gemma, PiXtral 12B) for lightweight use cases 🎯 LRMs (OpenAI o3, DeepSeek) for advanced reasoning Agents can analyze prompts, break them down, and decide which tools/APIs to call. 🔹 5. Agent Interoperability – Teams of Agents 🤝👥 No single agent can do it all. With protocols like MCP, multiple agents—Sales, Docs, Support—collaborate seamlessly. This is where multi-agent ecosystems shine 🌟. 🚀 Why This Blueprint Matters When these layers combine, AI agents can: ✅ Adapt to any input ✅ Make reliable, context-aware decisions ✅ Collaborate like real teams ✅ Scale safely with guardrails & orchestration This is how we move from fragile prototypes → production-ready agent ecosystems. 🌍💡 #AgenticAI #ArtificialIntelligence #EnterpriseAI #Innovation #FutureOfWork

It's not just about how we build AI — it's how we adapt it. If your system can't re-route, re-weight, or re-remember in real time, you're building a museum piece, not a tool. Insight: OrKa 1.5 points to a simple truth: as inputs become more dynamic, static pipelines break. Adaptive orchestration lets systems adjust policies, memories, and modules on the fly so they manage tasks more efficiently and robustly. For solopreneurs and engineers, that means getting more value from smaller models by improving the system that coordinates them — not only by scaling sizes. OrKa reasoning connection: OrKa's focus on modular cognition, explainability, and observability makes adaptive orchestration practical. Treat cognition as replaceable components (planner, memory, executor) and add a lightweight orchestration layer that monitors signals and reroutes work. This makes behavior traceable, debuggable, and auditable — exactly what explainable AI needs. Actionable takeaways: • Re-evaluate: are you optimizing models or the orchestration policy that uses them? • Start small: implement a policy layer that can switch strategies based on simple signals (latency, confidence, cost). • Measure observability: add traces for decisions so you can iterate systematically. Contrarian nudge: before buying compute or tuning larger models, ask whether smarter orchestration would solve your problem. Explore OrKa's approach and get hands-on: https://lnkd.in/djP4E9_v

🌐 The Future of AI: Why Agentic Models + LangGraph Matter Most of us have interacted with LLMs in a prompt → response fashion. Useful, yes—but limited. The real leap forward comes with agentic models. 🔹 What are Agentic Models? Instead of just answering, agentic models can: Reason through multi-step problems Plan sequences of actions toward a goal Take action by calling APIs, querying databases, or triggering workflows Adapt to feedback dynamically This shift transforms LLMs from “smart assistants” into autonomous problem-solvers. 🔹 Enter LangGraph One of the challenges with agentic systems is orchestration—how do you manage multiple agents, ensure a reliable state, and prevent things from going off track? That’s where LangGraph shines: It lets you design agent workflows as graphs—nodes for agents/tools, edges for flow of information. It provides memory and state management, so agents don’t lose context across steps. It supports branching, retries, and control flows, making systems more predictable. It’s modular—you can plug in different LLMs, APIs, or even other agents. 🔹 Why This Matters For enterprises, the combination of agentic models + LangGraph means: ✅ Building AI that can handle end-to-end business processes, not just snippets of text. ✅ Reduced hallucinations thanks to structured reasoning and explicit tool use. ✅ Faster experimentation with multi-agent architectures without reinventing orchestration logic. ✅ A path toward trustworthy AI systems that are observable, and scalable. 💡 The takeaway: Agentic models bring autonomy. LangGraph brings reliability and structure. Together, they represent the next stage of AI systems—moving from “talking machines” to collaborative digital workers.