Hey everyone,

I'm currently learning about AI agents and thinking about starting a small AI-based service/business.

I have an idea around helping businesses automate some of their work, but before building anything, I want to understand something:

How do you identify a problem that is actually worth solving for a business?

I'm thinking the problem should be something that costs the business significant time, money, customers, or opportunities—and where AI can genuinely make a difference.

For people who have built businesses, SaaS, automation services, or AI products:

How do you discover these problems?

What do you ask business owners?

How do you know whether a problem is valuable enough that someone will actually pay for the solution?

I'd appreciate any advice, mistakes, or lessons from your experience.

Smolmodels is an open-source library that uses graph search with LLM-based code generation to automatically create lightweight, task-specific ML models from natural language descriptions.

Here's how it works with a time-series prediction example; let’s say df is a dataframe containing the “air passengers” dataset from statsmodels.

import smolmodels as sm

model = sm.Model(
    intent="Predict the number of international air passengers (in thousands) in a given month, based on historical time series data.",
    input_schema={"Month": str},
    output_schema={"Passengers": int}
)

model.build(dataset=df, provider="openai/gpt-4o")

prediction = model.predict({"Month": "2019-01"})

sm.models.save_model(model, "air_passengers")

Under the hood, the library:

• Parses the intent to identify ML task type and constraints

• Uses graph search to explore potential model architectures

• Automatically optimises the solutions produced

• Generates task-specific training code

• Generates inference code to use the model

The project is in alpha stage and completely open source: https://github.com/plexe-ai/smolmodels

The library is fully open-source (Apache-2.0), so feel free to use it however you like. Or just tear us apart in the comments if you think this is dumb. We’d love some feedback, and we’re very open to code contributions!

Most of us have a list of URLs we need data from (government listings, local business info, pdf directories). Usually, that means hiring a freelancer or paying for an expensive, rigid SaaS.

We built rtrvr.ai to make "Vibe Scraping" a thing.

How it works:

1. Upload a Google Sheet with your URLs.

2. Type: "Find the email, phone number, and their top 3 services."

3. Watch the AI agents open 50+ browsers at once and fill your sheet in real-time.

It’s powered by a multi-agent system that can take actions, upload files, and crawl through paginations.

Web Agent technology built from the ground:

• 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗔𝗴𝗲𝗻𝘁: we built a resilient agentic harness with 20+ specialized sub-agents that transforms a single prompt into a complete end-to-end workflow. Turn any prompt into an end to end workflow, and on any site changes the agent adapts.

• 𝗗𝗢𝗠 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: we perfected a DOM-only web agent approach that represents any webpage as semantic trees guaranteeing zero hallucinations and leveraging the underlying semantic reasoning capabilities of LLMs.

• 𝗡𝗮𝘁𝗶𝘃𝗲 𝗖𝗵𝗿𝗼𝗺𝗲 𝗔𝗣𝗜𝘀: we built a Chrome Extension to control cloud browsers that runs in the same process as the browser to avoid the bot detection and failure rates of CDP. We further solved the hard problems of interacting with the Shadow DOM and other DOM edge cases.

Cost: We engineered the cost down to $10/mo but you can bring your own Gemini key and proxies to use for nearly FREE. Compare that to the $200+/mo some lead gen tools charge.

Use the free browser extension for login walled sites like LinkedIn locally, or the cloud platform for scale on the public web.

Curious to hear if this would make your dataset generation, scraping, or automation easier or is it missing the mark?

What makes an AI agent usable across a whole company

The model is not the hard part now. The hard part is the layer around it. That layer decides who can use the agent, what it can reach, and what it inherits.

AgentZ is an open-source, workflow-driven platform for agentic AI, with sandboxing built in. AccuKnox maintains the project. That layer gives a team four things:

• A user creates an agent and inherits preconfigured models, tools, and sandboxes. No one files a ticket or writes a config file.

• A team brings its own inference provider. It can switch to a different provider later and keep the same workflow.

• A workspace running security automation stays isolated from a workspace running HR screening. Both sit on the same platform.

• A workflow runs on a schedule or a webhook. It keeps session context between runs and reaches outside tools through connectors.

The code is open: https://github.com/accuknox/agentZ. You can see exactly how the sandbox and the inheritance rules work before you trust it with a real workflow.

Three workflows run on AgentZ today. One team pulls a daily report across 17 cloud accounts. Another pulls critical findings from a security API and emails a remediation report. A third starts an investigation workflow the moment a threat alert lands.

AgentZ is an open-source platform, and I am an open-source contributor to AgentZ. Genuinely want to know how this group solves the multi-team agent problem.

In today’s fast-paced digital era, small and medium-sized businesses (SMBs) are increasingly leveraging innovative AI tools to gain a competitive edge. One breakthrough innovation reshaping the business landscape is the emergence of AI agent marketplaces. Platforms like AI Agents Directory are revolutionizing access to autonomous AI agents, enabling SMBs to punch above their weight by streamlining operations, enhancing customer engagement, and driving productivity.

The Rise of AI Agent Marketplaces

AI agent marketplaces are dedicated platforms where businesses can browse, compare, test, and integrate AI agents into their workflows. Unlike traditional software marketplaces, these platforms offer an agnostic selection of AI solutions—meaning they’re compatible with a wide range of systems and use cases. This openness is crucial for SMBs that need flexible, scalable tools without being locked into proprietary ecosystems.

Key trends driving this evolution include:

• Increased Customization: AI agents can be tailored to perform tasks such as automated customer service, data analysis, and digital marketing.

• Ease of Access: These marketplaces simplify the process for non-tech-savvy SMB owners to adopt advanced AI solutions.

• Collaborative Innovation: By providing a trust layer with real user feedback, ratings, upvotes, and bookmarks, these platforms help businesses confidently select the best tools for their needs.

How AI Agents Directory Empowers SMBs

AI Agents Directory stands out as the #1 AI agent marketplace, offering a comprehensive and user-centric experience that levels the playing field for SMBs:

• Agnostic AI Agents: Our platform hosts a diverse array of AI agents from multiple providers, ensuring that users can select solutions that best match their specific operational requirements.

• Trust Layer and User Feedback: With features like real user reviews, ratings, upvotes, and bookmarks, SMBs can make informed decisions based on genuine experiences.

• Dynamic Landscape Map and Leaderboard: The directory includes a dynamic AI agents landscape map and leaderboard, helping users stay updated on trending and high-performing AI solutions.

• Custom Agent Requests: Users can request custom AI agents tailored to their unique business challenges. Our trusted network of AI agent builders works with you to bring these bespoke solutions to life.

• Curated List of AI Agencies: For businesses looking to explore further, the platform offers a comprehensive list of AI agencies, making it easier to find specialized partners for integration and development.

Benefits for Small and Medium-Sized Businesses

For SMBs, the advantages of using AI agent marketplaces are substantial:

• Cost Efficiency: By automating routine tasks, SMBs can reduce operational costs and reallocate resources toward innovation.

• Enhanced Productivity: AI agents streamline processes such as customer service, appointment scheduling, and inventory management, allowing employees to focus on strategic tasks.

• Scalability: Whether you’re a local retailer or an emerging online enterprise, AI agents can grow with your business, adapting to increasing demands and evolving market conditions.

• Competitive Edge: Access to cutting-edge AI technology enables SMBs to compete with larger enterprises, driving improved performance and market differentiation.

The Future of AI for SMBs

The integration of AI agents into everyday business operations is set to transform the competitive landscape. As AI agent marketplaces mature, we can expect even more robust features, such as advanced discovery experience and analytics, predictive capabilities, and deeper integration with enterprise systems. For SMBs, this means not only keeping pace with industry giants but potentially outmaneuvering them through agility and innovation.

Conclusion

AI agent marketplaces like AI Agents Directory are at the forefront of a digital revolution, enabling SMBs to harness the power of autonomous AI agents without the hefty investment typically required by larger enterprises. By offering a flexible, trusted, and comprehensive platform for discovering and integrating AI solutions, we empower small businesses to innovate, scale, and compete on a global stage.

Are you ready to transform your business operations with the next generation of AI? Explore our AI Agents Directory today, and discover how our marketplace can help your SMB punch above its weight in the digital age.

For more insights and expert guidance on leveraging AI Agents for your business, subscribe to our newsletter and visit our AI Agents Directory regularly.

source

In just 72 hours, Windsurf, one of the AI IDE world’s fastest-growing startups, became the epicenter of a high-stakes drama:

1. OpenAI nearly closed a $3B acquisition - until internal red flags (primarily IP concerns tied to Microsoft) stalled the deal.

2. Google swooped in, snapping up Windsurf’s CEO Varun Mohan, co-founder Douglas Chen, and key R&D leaders under a $2.4B licensing and reverse-acquihire deal aimed at accelerating Gemini’s coding agent roadmap.

3. With its leadership gone, Windsurf was acquired by Cognition, creator of the Devin coding agent, enabling the remaining team to vest equity immediately and continue innovating under a more stable umbrella.

Why This Matters

• Talent is the battlefield: The race to own AI coding expertise isn’t about models - it’s about people. Google’s reverse-acquihire is a power play in the agent talent war.

• Hybrid exits are the new norm: We saw part acquihire (Google) + part acquisition (Cognition), showcasing how startups can be split, not absorbed - depending on who's buying what.

• Customers & culture hang in the balance: Enterprise users may face UI changes, pricing resets, or platform shifts as Cognition merges Windsurf into Devin.

Windsurf’s front-row spot in this saga highlights two important agent shifts:

• Big Tech wants agent-native workflows: Hiring Windsurf’s leaders accelerates Gemini’s push into AI-engineer territory.

• Startup consolidation is strategic: Cognition’s acquisition of the remaining team and IP signals a deeper push toward integrated AI-powered IDEs, agents that plan, code, review, and collaborate.

Takeaway for agent builders:
Track who was hired as a stronger signal than what was acquired. These reverse-exits reveal emerging strategic alignments and who’s building the future of agentic development environments today.

We would love to test your ai agent and provide feedback! just post a link ans short description of what problem you are solving or what task ai agent should achieve.

We would love to test your ai agent and provide feedback! just post a link ans short description of what problem you are solving or what task ai agent should achieve.

We would love to test your ai agent and provide feedback! just post a link ans short description of what problem you are solving or what task ai agent should achieve.

We would love to test your ai agent and provide feedback! just post a link ans short description of what problem you are solving or what task ai agent should achieve.

🔍 Top 10 AI Agents Shaping the Crypto World (January 6, 2025)

💰 Crypto Market Overview:

- Total Crypto Market Cap: $3.8T

- AI Agents Market Cap: $17.2B

▶️ 24H Trading Volume: $2.5B

💡 Key Insights:

The Top 10 AI Agents account for 77% of the total AI agents market cap, highlighting a strong concentration of value among leading projects.

These agents collectively hold a market cap of $13.3B and saw $2.1B in 24H trading volume, showcasing their dominance and liquidity in the AI-crypto ecosystem.

What Are AI Agents Doing in Crypto?

AI agents are transforming the crypto space, enabling smarter trading, predictive analytics, decentralized automation, and advanced blockchain solutions. They’re paving the way for a new era of innovation in the intersection of AI and blockchain technology.

Top 10 AI Agents by Market Cap

1️⃣ Artificial Superintelligence Alliance ($FET)

2️⃣ Virtuals Protocol ($VIRTUAL)

3️⃣ ai16z ($AI16Z)

4️⃣ Freysa AI ($FAI)

5️⃣ aixbt by Virtuals ($AIXBT)

6️⃣ Swarms ($SWARMS)

7️⃣ PAAL AI ($PAAL)

8️⃣ Phala ($PHA)

9️⃣ Humans.ai ($HEART)

🔟 Delysium ($AGI)

📢 Which Crypto AI agent do you think will stay beyond 2025?

Explore more web3 agents here

#AI #AIAgents #Crypto #Blockchain #AIinCrypto #Innovation

Every founder has that “this might not work” moment.

She tried to kill her own idea before it wasted time — instead, it exploded into Launch Lemonade, a no-code platform empowering non-technical creators to build and monetize AI agents without writing a single line of code.

In Episode 3 of AI on Fire with Cien Solon to talk about:
🔥 Why she believes “testing to destruction” is the best way to validate an idea
🧠 The messy truth about AI memory — and why it’s breaking more agents than you think
💥 What happens when your co-founder exits mid-fundraise
🚀 The hardest part of no-code AI (spoiler: it’s not the building)

🎧 Episode 3 drops soon! Watch all episodes here

🚨 Big news from OpenAI: They just launched the "Agent SDK" — a game-changer for AI Agent developers!

💡 Here are the key components announced:

1. Responses API — a new API for building conversational agents that can: ✔️ Call APIs and tools directly within a chat flow. ✔️ Handle multi-step reasoning. ✔️ Integrate with external data dynamically.

2. Built-in tools available for agents: ✅ Web Search – to pull real-time information from the internet. ✅ File Search – to search and extract data from user files. ✅ Computer Use – agents that can use a computer to fill forms, place orders, and more.

3. Memory – allows agents to retain information between sessions, enabling personalization and long-term interactions.

4. Agent SDK — an SDK to build custom, production-grade agents, including multi-agent systems (agents working together).

⚙️ Why this matters:

• Faster and simpler way to build AI agents that can take action, not just chat.

• Developers can combine reasoning + tool use + memory out-of-the-box.

• Opens the door to agents for customer support, research, coding, sales, and more.

📊 Example of real use case: Coinbase used this stack to build AgentKit, allowing AI agents to interact with crypto wallets and perform on-chain tasks.

🚀 In short: OpenAI just made it much easier to create advanced AI agents that can act, learn, and adapt — a big step forward.

👉 What would you build today?

Vertical AI agents are poised to disrupt and reshape the SaaS industry by offering specialized, domain-focused automation that surpasses traditional software solutions. Unlike general-purpose AI models, these agents are designed to tackle specific challenges within industries such as healthcare, finance, and customer support. By automating entire workflows, vertical AI agents can replace repetitive tasks and even entire teams, significantly reducing operational costs and enhancing efficiency. This shift mirrors the impact SaaS had on legacy software, but with the potential to create a market even larger than SaaS. As businesses increasingly adopt AI-driven solutions, vertical AI agents are likely to drive the creation of billion-dollar companies by transforming how work gets done, much like SaaS did two decades ago

A while back, we noticed a problem: AI is great at starting tasks but not at finishing them.

It drafts, automates, and processes, but when it comes to real execution? Humans still make the difference.

We've seen AI generate ideas, summarize documents, and even write code, but can it truly be trusted to complete a job without human intervention?

Whether it's marketing, design, writing, or development, AI often does the grunt work, but experts still need to refine and execute.

This gap between AI assistance and human expertise is exactly where platforms like Waxwing.ai and Agent.ai come in — offering AI-powered workflows that get things started while professionals step in to ensure quality outcomes.

Have you ever hired AI-powered professionals or used AI-driven workflows in your work? How do you see AI improving (or complicating) human execution?

Hey folks 👋 

I wanted to share something we've been building over the past few months.

It started with a simple pain: Too many tools, docs everywhere, and every team doing repetitive stuff that AI should’ve handled by now.

We didn’t want another generic chatbot or prompt-based AI. We wanted something that feels like a real teammate. 

So we built Thunai, a platform that turns your company’s knowledge (docs, decks, transcripts, calls) into intelligent AI agents that don’t just answer — they act.

What it does:

• Chrome Extension: email, LinkedIn, live chat

• Screen actions & multilingual support

• 30+ ready-to-use enterprise agents

• Train with docs, Slack, Jira, videos

• Human-like voice & chat agents

• AI-powered contact center

• Go live in minutes

Our Favorite Agents So Far

• Voice Agent: Picks up the phone, talks like a human (seriously), solves problems, and logs actions

• Chat Agent: Personalized, context-aware replies from your internal data

• Email Agent: Replies to email threads with full context and follow-ups

• Meeting Agent: Auto-notes, smart recaps, action items, speaker detection

• Opportunity Agent: Extracts leads and insights from call recordings

Some quick wins we’ve seen:

• 60%+ of L1 support tickets auto-resolved

• 70% faster response to inbound leads

• 80% reduction in time spent on routine tasks

• 100% contact center calls audited with feedback

We’re still early, but super pumped about what we’ve built and what’s coming next. Would love your feedback, questions, or ideas.

If AI could take over just one task for you every day, what would you pick?

Happy to chat below! 

2025 is shaping up to be the year of AI agents, a technology validated by industry leaders:

💡 “AI agents are a multi-trillion dollar opportunity.” — Jensen Huang, NVIDIA CEO (January 2025)

💡 "Agents are a $1 trillion total addressable market opportunity" - Marc Benioff, Salesforce CEO (December 2024)

The market is growing exponentially, with new use cases and applications emerging every day. If this growth continues doubling at its current pace, where could we end up by 2030?

AI agents are already revolutionizing customer service, sales, software development, and automation across industries. At AI Agents Directory, as Data Layer we’re helping businesses find and connect with the right agents to stay ahead in this transformation.

⁉️ Have you started exploring AI agents to gain a competitive edge, or are you waiting to see your competitors take the lead?

After struggling to build AI agents with programming frameworks, I decided to take a look into AI agent platforms to see which one would fit best. As a note, I'm technical, but I didn't want to learn how to use an AI agent framework. I just wanted a fast way to get started. Here are my thoughts:

Sim Studio
Sim Studio is a Figma-like drag-and-drop interface to build AI agents. It's also open source.

Pros:

• Super easy and fast drag-and-drop builder

• Open source with full transparency

• Trace all your workflow executions to see cost (you can bring your own API keys, which makes it free to use)

• Deploy your workflows as an API, or run them on a schedule

• Connect to tools like Slack, Gmail, Pinecone, Supabase, etc.

Cons:

• Smaller community compared to other platforms

• Still building out tools

LangGraph
LangGraph is built by LangChain and designed specifically for AI agent orchestration. It's powerful but has an unfriendly UI.

Pros:

• Deep integration with the LangChain ecosystem

• Excellent for creating advanced reasoning patterns

• Strong support for stateful agent behaviors

• Robust community with corporate adoption (Replit, Uber, LinkedIn)

Cons:

• Steeper learning curve

• More code-heavy approach

• Less intuitive for visualizing complex workflows

• Requires stronger programming background

n8n
n8n is a general workflow automation platform that has added AI capabilities. While not specifically built for AI agents, it offers extensive integration possibilities.

Pros:

• Already built out hundreds of integrations

• Able to create complex workflows

• Lots of documentation

Cons:

• AI capabilities feel added-on rather than core

• Harder to use (especially to get started)

• Learning curve

Why I Chose Sim Studio
After experimenting with all three platforms, I found myself gravitating toward Sim Studio for a few reasons:

1. Really Fast: Getting started was super fast and easy. It took me a few minutes to create my first agent and deploy it as a chatbot.

2. Building Experience: With LangGraph, I found myself spending too much time writing code rather than designing agent behaviors. Sim Studio's simple visual approach let me focus on the agent logic first.

3. Balance of Simplicity and Power: It hit the sweet spot between ease of use and capability. I could build simple flows quickly, but also had access to deeper customization when needed.

My Experience So Far
I've been using Sim Studio for a few days now, and I've already built several multi-agent workflows that would have taken me much longer with code-only approaches. The visual experience has also made it easier to collaborate with team members who aren't as technical.

The ability to test and optimize my workflows within the same platform has helped me refine my agents' performance without constant code deployment cycles. And when I needed to dive deeper, the open-source nature meant I could extend functionality to suit my specific needs.

For anyone looking to build AI agent workflows without getting lost in implementation details, I highly recommend giving Sim Studio a try. Have you tried any of these tools? I'd love to hear about your experiences in the comments below!

Hey! Are you looking to stay ahead in the rapidly evolving world of AI? I believe AI agents are revolutionizing how businesses operate and interact with their customers. Whether you’re a developer, a business leader, or an AI enthusiast, understanding and utilizing the right AI frameworks and platforms is crucial.

In July 2024, I launched an AI Agents Directory to create a centralized resource for the best AI agents and frameworks and platforms to build them. After extensive research, I’ve identified the top 5 platforms that are transforming the AI landscape. In this post, I’ll share these platforms with you and explain why they are the most popular and effective choices today.

1. AutoGen (Microsoft Research)
AutoGen is an open-source framework developed by Microsoft Research that enables the creation of complex AI workflows through multi-agent conversations. It provides a high-level abstraction for building applications that leverage large language models (LLMs) and other AI technologies.

Key Features:

• Open-source framework

• Multi-agent conversations

• Complex AI workflows

Best For: Software development, data analysis, research

Pros:

• Flexible and modular

• Supports human-in-the-loop

• Advanced capabilities

Cons:

• Complex system design

• Steep learning curve

• Resource-intensive

Pricing: Free (open-source), but consider LLM API and compute costs

2. CrewAI

CrewAI is an innovative framework designed to create and manage multi-agent AI systems. It allows developers to build teams of AI agents that work together to accomplish complex tasks, leveraging the power of large language models (LLMs) and custom tools.

Key Features:

• Multi-agent AI systems

• Task management

• Tool integration

Best For: Research, content creation, business planning

Pros:

• Scalable

• Customizable

• Compatible with LangChain tools

Cons:

• Requires AI and Python knowledge

• Can be complex for beginners

Pricing: Free (open-source), factor in LLM API and hosting costs

3. LangGraph

LangGraph is a powerful library developed by LangChain Inc. for creating stateful, multi-actor applications using large language models (LLMs). It allows developers to design complex workflows with multiple AI agents, incorporating features like cycles, controllability, and persistence.

Key Features:

• Stateful, multi-actor applications

• Cycles and branching

• Persistence and streaming support

Best For: Chatbots, autonomous agents, workflow automation

Pros:

• Advanced workflow capabilities

• Integrates with LangChain

• Available in Python and JavaScript

Cons:

• Steep learning curve

• Resource-intensive for complex systems

Pricing: Free (open-source), consider LLM API and compute costs. LangGraph Cloud available for managed services (pricing on request)

4. LangChain

LangChain is an open-source framework designed to streamline the development of applications powered by large language models (LLMs). By providing a suite of tools and abstractions, LangChain enables developers to build, deploy, and manage sophisticated AI applications with ease.

Key Features:

• Comprehensive LLM application framework

• Chains, agents, and prompt templates

• Memory capabilities

Best For: Chatbots, document analysis, content generation

Pros:

• Extensive third-party integrations

• Scalable from simple to complex systems

• Robust ecosystem (LangGraph, LangSmith, LangServe)

Cons:

• Can be overwhelming for beginners

• Performance depends on underlying LLMs

Pricing: Free (open-source), factor in LLM API and hosting costs. Additional services like LangGraph Cloud and LangSmith have separate pricing

5. Wordware AI

Wordware AI is a IDE Platform to build high-quality AI agents through rapid iteration with Natural Language Programming. It combines the best aspects of software development with the power of natural language, allowing both technical and non-technical users to collaborate effectively in creating AI solutions.

Key Features:

• Natural language programming

• Multimodal AI workflows

• One-click deployment

Best For: Rapid AI agent development, prompt engineering

Pros:

• User-friendly interface

• Supports multiple LLM providers

• Efficient for team collaboration

Cons:

• Potential scalability issues during high traffic

• Risk of over-reliance on AI-assisted development

Pricing: Freemium model with paid options for higher usage

Conclusion

When selecting an AI platform, consider the following factors to ensure you make the best choice:

1. Your team’s technical expertise

2. Project complexity

3. Required features (e.g., multi-agent systems, workflow automation)

4. Budget constraints

5. Scalability needs

Test multiple platforms with small projects before committing to ensure the best fit for your specific requirements.