How AI is changing the role of software developers

Bringing Serious AI to Manufacturing

Check out the post below. It’s an interesting change as few AI experts with their Python skills know production software development or have domain expertise (CAD for me). The good news is that serious software developers and industry experts will still have a big role if they are learning AI methodologies (agents, MCP, getting consistently good responses). Put another way, it is a mechanical engineer who became an expert with AI who will be the most valuable next year. It will be the same for top software engineers. Data scientists will still be valuable but companies learn it not enough.

Alex Wang

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!

Building AI agents = 5% AI + 95% software engineering. Not exact ratio, but you get the point. We don’t just “build an agent”; we architect a system and let the AI sit inside it. Enterprise-ready agent platforms are classic software + reliability engineering work: ✅ Identity and access management ✅ Document filtering and governance (ACLs, redaction, PII masking) ✅ Schema mapping and data contracts ✅ Human-in-the-loop escalation ✅ Infra that scales across vector and SQL ✅ Observability, evals, guardrails, cost controls, and audit logs Think of agents like APIs that can reason, not magic. They still need: - Fine-grained access control - Storage that separates structured and unstructured knowledge - Tracing, fallback routing, and lineage - Flows that connect document pipelines with model orchestration (MCP, tools, integrations) Before you tune prompts, build the foundations. If you’re exploring agent workflows, check out our open-source platform, a lightweight framework we use to run multi-agent systems: GitHub: https://bit.ly/4kzE1Mt Explore our public investment: 🔗https://bit.ly/41JeDNO __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #AIagents #AgenticAI #EnterpriseAI #Technology

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Nick Sonnentag

Delivering Advanced Materials Solutions

What programming language did the mechanical engineer learn to be useful? What I’m getting from this post is that using AI for coding isn’t really great unless you (a human) can troubleshoot the code created - because there are still issues in AI code development. If coding is moving similar to medical or mathematical performance the AI may be better than a human soon - so I’m not sure this is really correct.

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Alex Wang Alex Wang is an Influencer

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Building AI agents = 5% AI + 95% software engineering. Not exact ratio, but you get the point. We don’t just “build an agent”; we architect a system and let the AI sit inside it. Enterprise-ready agent platforms are classic software + reliability engineering work: ✅ Identity and access management ✅ Document filtering and governance (ACLs, redaction, PII masking) ✅ Schema mapping and data contracts ✅ Human-in-the-loop escalation ✅ Infra that scales across vector and SQL ✅ Observability, evals, guardrails, cost controls, and audit logs Think of agents like APIs that can reason, not magic. They still need: - Fine-grained access control - Storage that separates structured and unstructured knowledge - Tracing, fallback routing, and lineage - Flows that connect document pipelines with model orchestration (MCP, tools, integrations) Before you tune prompts, build the foundations. If you’re exploring agent workflows, check out our open-source platform, a lightweight framework we use to run multi-agent systems: GitHub: https://bit.ly/4kzE1Mt Explore our public investment: 🔗https://bit.ly/41JeDNO __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #AIagents #AgenticAI #EnterpriseAI #Technology
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Tailored Techworks

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Loved Alex Wang's clear split between AI agents (task executors) and Agentic AI (orchestration layer) for the enterprise. +1. Here’s what we’re seeing in real environments: What breaks first in “one agent does it all”? • Single point of failure across systems • Memory drift between projects/compliance zones • Unpredictable latency when tasks chain across APIs What works better in production: governed multi-agent orchestration with a supervisor layer and folder-scoped memory per project. Agents remain narrow; orchestration enforces policy, telemetry, and graceful degradation. Why now (numbers, not hype): • Autonomous AI/Agents market: $4.8B → $28.5B by 2028 (43% CAGR). (GlobeNewswire, Prnewswire) • AI orchestration platforms: $5.8B (2024) → $48.7B (2034, 23.7% CAGR). (Market.us Scoop) • GenAI impact: $2.6T–$4.4T annual value potential—captured by reliable workflows, not hero agents. -( McKinsey & Company VentureBeat) Our approach in one line: a fault-aware loop that builds → runs → diagnoses → patches automatically, with audit logs and guardrails, so results are repeatable, testable, and safe (vs. clever-but-fragile autonomy). (Good primer on “orchestration vs agentic control” here. ) - CIO Aisera: Best Agentic AI For Enterprise What’s your gnarliest orchestration bottleneck—policy enforcement, memory isolation, or cross-system latency? #AgenticAI #AIagents #AIOperations #AIOrchestration #EnterpriseAI #LLMOps #AIGovernance #MLOps
Alex Wang Alex Wang is an Influencer

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!
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Building AI agents = 5% AI + 95% software engineering. Not exact ratio, but you get the point. We don’t just “build an agent”; we architect a system and let the AI sit inside it. Enterprise-ready agent platforms are classic software + reliability engineering work: ✅ Identity and access management ✅ Document filtering and governance (ACLs, redaction, PII masking) ✅ Schema mapping and data contracts ✅ Human-in-the-loop escalation ✅ Infra that scales across vector and SQL ✅ Observability, evals, guardrails, cost controls, and audit logs Think of agents like APIs that can reason, not magic. They still need: - Fine-grained access control - Storage that separates structured and unstructured knowledge - Tracing, fallback routing, and lineage - Flows that connect document pipelines with model orchestration (MCP, tools, integrations) Before you tune prompts, build the foundations. If you’re exploring agent workflows, check out our open-source platform, a lightweight framework we use to run multi-agent systems: GitHub: https://bit.ly/4kzE1Mt Explore our public investment: 🔗https://bit.ly/41JeDNO __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #AIagents #AgenticAI #EnterpriseAI #Technology
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Ayesha Shahid

AI Engineer ||Data Scientist || SQL || || NLP ||ML|| Langchain || Lang graph || MCP || Crewai || Ai agents || Rag
4w
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Reality Check on AI Agency Sales !!!!!!! Many AI agencies focus on surface-level tools like chatbots or voice agents, but businesses are willing to pay premium prices for comprehensive AI solutions that solve real operational challenges at scale. Example: A $2 million ARR SaaS company preferred spending $20,000 on a solution that increased revenue by $50,000/month rather than $2,000 on a chatbot saving 5 hours of weekly support time totally agreed with Alex Wang
Alex Wang Alex Wang is an Influencer

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!
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Building AI agents = 5% AI + 95% software engineering. Not exact ratio, but you get the point. We don’t just “build an agent”; we architect a system and let the AI sit inside it. Enterprise-ready agent platforms are classic software + reliability engineering work: ✅ Identity and access management ✅ Document filtering and governance (ACLs, redaction, PII masking) ✅ Schema mapping and data contracts ✅ Human-in-the-loop escalation ✅ Infra that scales across vector and SQL ✅ Observability, evals, guardrails, cost controls, and audit logs Think of agents like APIs that can reason, not magic. They still need: - Fine-grained access control - Storage that separates structured and unstructured knowledge - Tracing, fallback routing, and lineage - Flows that connect document pipelines with model orchestration (MCP, tools, integrations) Before you tune prompts, build the foundations. If you’re exploring agent workflows, check out our open-source platform, a lightweight framework we use to run multi-agent systems: GitHub: https://bit.ly/4kzE1Mt Explore our public investment: 🔗https://bit.ly/41JeDNO __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #AIagents #AgenticAI #EnterpriseAI #Technology
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Anita Alexander

Vice President - Engineering | Data & AI leader | Quantum Curious
3w Edited
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Agentic AI is not a standalone marvel but a powerful addition to an ecosystem deeply rooted in foundational engineering excellence. Agentic AI systems—those that can autonomously pursue goals, make decisions, and adapt, are "Built on Foundational Engineering Pillars", and requires -> Robust data pipelines for context and learning. -> Scalable infrastructure to support real-time reasoning and action. -> Secure environments to ensure safe autonomy. -> Reliable software engineering for modularity, observability, and resilience. Without these, agentic behavior becomes brittle or unsafe. Agentic AI doesn’t operate in isolation. It "Thrives in an Ecosystem of Interoperability" and needs -> Access to APIs, tools, and systems to take meaningful actions. -> Integration with governance frameworks to ensure ethical boundaries. -> Feedback loops from users and systems to refine its behavior. This makes it a system-level capability, not a plug-and-play feature. Alex Wang - thanks for this post! You are hitting the right chord with this.
Alex Wang Alex Wang is an Influencer

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!
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Building AI agents = 5% AI + 95% software engineering. Not exact ratio, but you get the point. We don’t just “build an agent”; we architect a system and let the AI sit inside it. Enterprise-ready agent platforms are classic software + reliability engineering work: ✅ Identity and access management ✅ Document filtering and governance (ACLs, redaction, PII masking) ✅ Schema mapping and data contracts ✅ Human-in-the-loop escalation ✅ Infra that scales across vector and SQL ✅ Observability, evals, guardrails, cost controls, and audit logs Think of agents like APIs that can reason, not magic. They still need: - Fine-grained access control - Storage that separates structured and unstructured knowledge - Tracing, fallback routing, and lineage - Flows that connect document pipelines with model orchestration (MCP, tools, integrations) Before you tune prompts, build the foundations. If you’re exploring agent workflows, check out our open-source platform, a lightweight framework we use to run multi-agent systems: GitHub: https://bit.ly/4kzE1Mt Explore our public investment: 🔗https://bit.ly/41JeDNO __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #AIagents #AgenticAI #EnterpriseAI #Technology
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Sergio Valladares

Chief Transformation Officer (CTO) & Director General | Liderando la Transformación Digital y el Crecimiento Operativo
3w
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The LLMs are just an element (a critical one, for sure) but AI is not mAgIc, is tons of work.
Alex Wang Alex Wang is an Influencer

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!
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Building AI agents = 5% AI + 95% software engineering. Not exact ratio, but you get the point. We don’t just “build an agent”; we architect a system and let the AI sit inside it. Enterprise-ready agent platforms are classic software + reliability engineering work: ✅ Identity and access management ✅ Document filtering and governance (ACLs, redaction, PII masking) ✅ Schema mapping and data contracts ✅ Human-in-the-loop escalation ✅ Infra that scales across vector and SQL ✅ Observability, evals, guardrails, cost controls, and audit logs Think of agents like APIs that can reason, not magic. They still need: - Fine-grained access control - Storage that separates structured and unstructured knowledge - Tracing, fallback routing, and lineage - Flows that connect document pipelines with model orchestration (MCP, tools, integrations) Before you tune prompts, build the foundations. If you’re exploring agent workflows, check out our open-source platform, a lightweight framework we use to run multi-agent systems: GitHub: https://bit.ly/4kzE1Mt Explore our public investment: 🔗https://bit.ly/41JeDNO __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #AIagents #AgenticAI #EnterpriseAI #Technology
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Hoang Dang

Co-Founder | CEO at dizim
3w
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AI Agents Are Everywhere. But They Don't Work Efficiently Together Until You Build The Foundations Many thanks to Alex for the concise summary and practical architecture design foundation.
Alex Wang Alex Wang is an Influencer

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!
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Building AI agents = 5% AI + 95% software engineering. Not exact ratio, but you get the point. We don’t just “build an agent”; we architect a system and let the AI sit inside it. Enterprise-ready agent platforms are classic software + reliability engineering work: ✅ Identity and access management ✅ Document filtering and governance (ACLs, redaction, PII masking) ✅ Schema mapping and data contracts ✅ Human-in-the-loop escalation ✅ Infra that scales across vector and SQL ✅ Observability, evals, guardrails, cost controls, and audit logs Think of agents like APIs that can reason, not magic. They still need: - Fine-grained access control - Storage that separates structured and unstructured knowledge - Tracing, fallback routing, and lineage - Flows that connect document pipelines with model orchestration (MCP, tools, integrations) Before you tune prompts, build the foundations. If you’re exploring agent workflows, check out our open-source platform, a lightweight framework we use to run multi-agent systems: GitHub: https://bit.ly/4kzE1Mt Explore our public investment: 🔗https://bit.ly/41JeDNO __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #AIagents #AgenticAI #EnterpriseAI #Technology
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Salamat Suerkul
3w Edited
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So true — building AI agents isn’t just prompt magic. It’s serious backend engineering, orchestration, access control, and infrastructure work. I learned this the hard way while experimenting with agent workflows for trucking and small business use cases. The “agent” is just the tip — the system it runs in is the real iceberg. Highly recommend checking out the open-source framework Alex Wang shared.
Alex Wang Alex Wang is an Influencer

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!
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Building AI agents = 5% AI + 95% software engineering. Not exact ratio, but you get the point. We don’t just “build an agent”; we architect a system and let the AI sit inside it. Enterprise-ready agent platforms are classic software + reliability engineering work: ✅ Identity and access management ✅ Document filtering and governance (ACLs, redaction, PII masking) ✅ Schema mapping and data contracts ✅ Human-in-the-loop escalation ✅ Infra that scales across vector and SQL ✅ Observability, evals, guardrails, cost controls, and audit logs Think of agents like APIs that can reason, not magic. They still need: - Fine-grained access control - Storage that separates structured and unstructured knowledge - Tracing, fallback routing, and lineage - Flows that connect document pipelines with model orchestration (MCP, tools, integrations) Before you tune prompts, build the foundations. If you’re exploring agent workflows, check out our open-source platform, a lightweight framework we use to run multi-agent systems: GitHub: https://bit.ly/4kzE1Mt Explore our public investment: 🔗https://bit.ly/41JeDNO __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #AIagents #AgenticAI #EnterpriseAI #Technology
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Padarbinda Das

Solutions Architect | Technical Architect | AI | MS Azure | Power Platform | AWS | Google Cloud | Microservices | Java | Scaled Agile
3w
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The power of AI is real. However, it is not panacea for everything. Agentic AI is not a magic wand. The expecations have to be realistic. While it sounds easier to build a POC, productionising a POC and realising the value is a different ball game altogether. Alex Wang very relevant illustation.
Alex Wang Alex Wang is an Influencer

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!
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Building AI agents = 5% AI + 95% software engineering. Not exact ratio, but you get the point. We don’t just “build an agent”; we architect a system and let the AI sit inside it. Enterprise-ready agent platforms are classic software + reliability engineering work: ✅ Identity and access management ✅ Document filtering and governance (ACLs, redaction, PII masking) ✅ Schema mapping and data contracts ✅ Human-in-the-loop escalation ✅ Infra that scales across vector and SQL ✅ Observability, evals, guardrails, cost controls, and audit logs Think of agents like APIs that can reason, not magic. They still need: - Fine-grained access control - Storage that separates structured and unstructured knowledge - Tracing, fallback routing, and lineage - Flows that connect document pipelines with model orchestration (MCP, tools, integrations) Before you tune prompts, build the foundations. If you’re exploring agent workflows, check out our open-source platform, a lightweight framework we use to run multi-agent systems: GitHub: https://bit.ly/4kzE1Mt Explore our public investment: 🔗https://bit.ly/41JeDNO __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #AIagents #AgenticAI #EnterpriseAI #Technology
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Hardik Chauhan

Program Manager at Bankai|SAFe® 6 Agilist | Telecom BSS /OSS |Fintech Solutions | And sport enthusiastic !
3w
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if you want quantifiable, ROI from agentic AI in enterprise grade ecosystems, you need to understand core software engineering !
Alex Wang Alex Wang is an Influencer

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!
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Building AI agents = 5% AI + 95% software engineering. Not exact ratio, but you get the point. We don’t just “build an agent”; we architect a system and let the AI sit inside it. Enterprise-ready agent platforms are classic software + reliability engineering work: ✅ Identity and access management ✅ Document filtering and governance (ACLs, redaction, PII masking) ✅ Schema mapping and data contracts ✅ Human-in-the-loop escalation ✅ Infra that scales across vector and SQL ✅ Observability, evals, guardrails, cost controls, and audit logs Think of agents like APIs that can reason, not magic. They still need: - Fine-grained access control - Storage that separates structured and unstructured knowledge - Tracing, fallback routing, and lineage - Flows that connect document pipelines with model orchestration (MCP, tools, integrations) Before you tune prompts, build the foundations. If you’re exploring agent workflows, check out our open-source platform, a lightweight framework we use to run multi-agent systems: GitHub: https://bit.ly/4kzE1Mt Explore our public investment: 🔗https://bit.ly/41JeDNO __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #AIagents #AgenticAI #EnterpriseAI #Technology
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Renewable Success

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3w
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Building AI agents is a great tool for businesses to improve productivity. By focusing on these steps in this post, businesses can move beyond a reactive use of AI and fully integrate agent-driven actions across their operations, gaining a significant competitive advantage. 𝚃̲𝚑̲𝚎̲ ̲𝚅̲𝚊̲𝚕̲𝚞̲𝚎̲ ̲𝚘̲𝚏̲ ̲𝙰̲𝙸̲ ̲𝙰̲𝚐̲𝚎̲𝚗̲𝚝̲𝚜̲ ̲𝚏̲𝚘̲𝚛̲ ̲𝙱̲𝚞̲𝚜̲𝚒̲𝚗̲𝚎̲𝚜̲𝚜̲𝚎̲𝚜̲ AI agents are more than just a tool for individual productivity; they are a strategic asset that can redefine how work gets done. Their value lies in their ability to: • Automate complex, multi-step workflows that were previously too ambiguous or dynamic for traditional automation. Unlike rule-based systems, AI agents can handle unpredictable situations by reasoning and adapting to new information. For example, a customer service agent can not only respond to a query but also access multiple systems to resolve an issue, update records, and send follow-up communications, all without human intervention. • Accelerate speed to value by eliminating delays and enabling parallel processing. By coordinating and executing multiple steps simultaneously, they can significantly reduce cycle times. This can be seen in areas like supply chain management, where agents can monitor data, anticipate disruptions, and reroute operations in real time. • Enhance decision-making by analyzing vast amounts of data at speeds that are impossible for humans. They can spot patterns, provide actionable insights, and even forecast market trends, empowering leaders to make more informed and strategic decisions. • Unlock new revenue streams by amplifying existing products or creating new ones. In e-commerce, for instance, an AI agent could proactively analyze user behavior and offer personalized upselling or cross-selling suggestions in real time. • Boost employee engagement and creativity by offloading repetitive, low-value tasks. This frees up human workers to focus on more complex, strategic work that requires creativity, critical thinking, and interpersonal skills, leading to a more engaged and productive workforce. This is a great post below on the building process
Alex Wang Alex Wang is an Influencer

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!
4w

Building AI agents = 5% AI + 95% software engineering. Not exact ratio, but you get the point. We don’t just “build an agent”; we architect a system and let the AI sit inside it. Enterprise-ready agent platforms are classic software + reliability engineering work: ✅ Identity and access management ✅ Document filtering and governance (ACLs, redaction, PII masking) ✅ Schema mapping and data contracts ✅ Human-in-the-loop escalation ✅ Infra that scales across vector and SQL ✅ Observability, evals, guardrails, cost controls, and audit logs Think of agents like APIs that can reason, not magic. They still need: - Fine-grained access control - Storage that separates structured and unstructured knowledge - Tracing, fallback routing, and lineage - Flows that connect document pipelines with model orchestration (MCP, tools, integrations) Before you tune prompts, build the foundations. If you’re exploring agent workflows, check out our open-source platform, a lightweight framework we use to run multi-agent systems: GitHub: https://bit.ly/4kzE1Mt Explore our public investment: 🔗https://bit.ly/41JeDNO __________ For more on AI and learning materials, plz check my previous posts. I share my journey here. Join me and let's grow together. Alex Wang #AIagents #AgenticAI #EnterpriseAI #Technology
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