Hi everyone,
So, first of all, I am posting this cause I'm GENUINELY worried with widespread layoffs looming that happened 2024, because of constant AI Agent architecture advancements, especially as we head into what many predict will be a turbulent 2025,
I felt compelled to share this knowledge, as 2025 will get more and more dangerous in this sense.
Understanding and building with AI agents isn't just about business – it's about equipping ourselves with crucial skills and intelligent tools for a rapidly changing world, and I want to help others navigate this shift. So, finally I got time to write this.
Okay, so it started two years ago,
For two years, I immersed myself in the world of autonomous AI agents.
My learning process was intense:
deep-diving into arXiv research papers,
consulting with university AI engineers,
reverse-engineering GitHub repos,
watching countless hours of AI Agents tutorials,
experimenting with Kaggle kernels,
participating in AI research webinars,
rigorously benchmarking open-source models
studying AI Stack framework documentations
Learnt deeply about these life-changing capabilities, powered by the right AI Agent architecture:
- AI Agents that plans and executes complex tasks autonomously, freeing up human teams for strategic work. (Powered by: Planning & Decision-Making frameworks and engines)
- AI Agents that understands and processes diverse data – text, images, videos – to make informed decisions. (Powered by: Perception & Data Ingestion)
- AI Agents that engages in dynamic conversations and maintains context for seamless user interactions. (Powered by: Dialogue/Interaction Manager & State/Context Manager)
- AI Agents that integrates with any tool or API to automate actions across your entire digital ecosystem. (Powered by: Tool/External API Integration Layer & Action Execution Module)
- AI Agents that continuously learns and improves through self-monitoring and feedback, becoming more effective over time. (Powered by: Self-Monitoring & Feedback Loop & Memory)
- AI Agents that works 24/7 and doesn't stop through self-monitoring and feedback, becoming more effective over time. (Powered by: Self-Monitoring & Feedback Loop & Memory)
P.S. (Note that these agents are developed with huge subset of the modern tools/frameworks, in the end system functions independently, without the need for human intervention or input)
As for 2025, here are the professions already in the crosshairs:
Technical Writers: Standardized documentation? AI can generate that crap all day long.
Junior Software Developers: Routine coding? Maintenance? Boilerplate? Agents are eating that for breakfast.
Content Writers: Basic articles, blog posts, SEO content? AI can churn that out faster and cheaper.
Copywriters: Advertising copy, promotional text? Agents are drafting that now.
SEO Specialists: Keyword research, data analysis? Automated.
Social Media Managers: Scheduling, basic content, performance tracking? Agents are taking over.
Digital Marketers: Ad optimization, campaign analytics? AI assistance, bordering on full automation.
Customer Support Representatives (Digital/Online: Basic queries? Chatbots are already handling it.)
Virtual Assistants: Scheduling, email, admin tasks? Agents are doing it.
Data Entry Clerks: Repetitive data input? Obvious automation target.
Quality Assurance (QA Testers: Manual QA? Automated testing frameworks are replacing that.)
Technical Support Specialists: Routine troubleshooting, FAQs? AI diagnostics are stepping in.
IT Helpdesk Technicians: First-level support? Automated systems are handling it.
Web Designers: Layouts, templates? AI-powered tools are generating that.
UI/UX Designers: Routine interface tweaks? Agents are automating that too.
Graphic Designers: Basic design elements, visual drafts? AI is churning that out.
Digital Artists: Template-based art production? Generative AI is changing the game.
Photo Editors: Routine corrections, batch adjustments? Automated.
Video Editors: Basic cutting, transitions, captions? AI systems are handling it.
Social Media Content Creators: Routine content for social platforms? Agents are on it.
Content Moderators: Automated filtering, sentiment analysis? AI is assisting or replacing human moderators.
E-commerce Listing Specialists: Product descriptions, catalog data? AI can generate that.
Data Analysts (Routine Reporting: Standard reports, dashboard updates? Automated with AI.)
Market Researchers: Basic data collection, surveys, trend identification? Increasingly automated.
Transcriptionists: Speech-to-text? AI is already dominant.
Translators (Routine Translation: Standard language conversion? Machine translation is there.)
Paralegals (Document Review: Document review, summarization? AI tools are used for that.)
Financial Analysts (Routine Analysis: Routine data processing, report generation? Agents are moving in.)
Tax Preparers: Standard tax computations, filing? Software is automating that.
Bookkeepers: Routine bookkeeping (data entry, reconciliation? AI accounting software is doing it.)
Proofreaders: Grammar, spell-check? AI is handling routine texts.
Content Curators: Aggregating, filtering content? AI can assist or replace.
Email Marketers: Automated campaigns, personalization? AI is streamlining it.
Chatbot Trainers / AI Annotators: Data annotation for AI training? The irony is thick.
Digital Advertising Specialists: Automated bidding, targeting? AI is optimizing campaigns.
Social Media Analysts: Parsing social data, trend forecasting? AI tools are used for that.
Online Community Managers: Routine moderation, engagement? AI can augment or replace.
Remote IT Administrators: Routine system monitoring, updates? Agents are stepping in.
Digital Product Managers: Routine roadmap updates, user feedback analysis? Automation is coming.
E-learning Course Developers: Content generation, customization? AI is impacting course development.
Anyway this is long list, but isn't just a list of random jobs. These are real professions, real people's livelihoods. And AI agents, built on modern stack, are coming for them.
Programming Language Usage in AI Agent Development (Estimated %):
Python: 85-90%
JavaScript/TypeScript: 5-10%
Other (Rust, Go, Java, etc.): 1-5%
→ Most of time, I use this stack for my own projects, and I'm happy to share it with you, cause I believe that this is the future, and we need to be prepared for it.
So, full stack, of how it is build you can find here:
https://docs.google.com/document/d/12SFzD8ILu0cz1rPOFsoQ7v0kUgAVPuD_76FmIkrObJQ/edit?usp=sharing
Edit: I will be adding in this doc from now on, many insights :)
✅ AI Agents Ecosystem Summary
✅ Learned Summary from +150 Research Papers: Building LLM Applications with Frameworks and Agents
⏳ + 20 Summaries Loading
Hope everyone will find it helpful, :) Upload this doc in your AI Google Studio and ask questions, I can also help if you have any question here in comments, cheers.
[link] [comments]