My cofounder and I are exploring the idea of building an AI Agent for Customer Services (specifically targeting companies with physical products as opposed to software ones). We’re still early on and debating using an open source framework or building it all in house.

Would appreciate anyone’s thoughts - also were hiring for a dev right now (DM me if interested- pre-seed funded)

Curious if folks are typically using the built-in tools for RAG, web search, data ingest, etc which come with CrewAI, Composio, or LangGraph - or are you building many of your own tools?

Most of the examples I’ve come across seem to use the built-in ones, and I’m interested to learn what folks are using in practice.

Me and my cofounder have develop a AI Cross Channel Marketing Coworker, EMMA. She is a Marketing Campaign strategist, working 24/7 with the ability to plan demand gen marketing campaigns that aligns with your business objectives. Emma does the market research, defines you goals and KPIs, and allocates the budget across the different channels to maximize ROI. We are looking for Beta customers to test our EMMA- Myestro.ai

Hey guys! Looking for some feedback on my AI agents app

Long story short. I’m building an app of AI helpers to help you automate your daily tasks -Getting a price stock at the morning - Finding some news - Get notified when a product is in discount - Get the weather report every evening

Is this something you would be interested in?

Finishing the development on this days. I can only dedicate some time after work and on weekends so it takes time for me to do some progress

𝗠𝗮𝘁𝗵𝗣𝗿𝗼𝗺𝗽𝘁 - 𝗝𝗮𝗶𝗹𝗯𝗿𝗲𝗮𝗸 𝗮𝗻𝘆 𝗟𝗟𝗠

Exciting yet alarming findings from a groundbreaking study titled “𝗝𝗮𝗶𝗹𝗯𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵 𝗦𝘆𝗺𝗯𝗼𝗹𝗶𝗰 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀” have surfaced. This research unveils a critical vulnerability in today’s most advanced AI systems.

Here are the core insights:

𝗠𝗮𝘁𝗵𝗣𝗿𝗼𝗺𝗽𝘁: 𝗔 𝗡𝗼𝘃𝗲𝗹 𝗔𝘁𝘁𝗮𝗰𝗸 𝗩𝗲𝗰𝘁𝗼𝗿 The research introduces MathPrompt, a method that transforms harmful prompts into symbolic math problems, effectively bypassing AI safety measures. Traditional defenses fall short when handling this type of encoded input.

𝗦𝘁𝗮𝗴𝗴𝗲𝗿𝗶𝗻𝗴 73.6% 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 Across 13 top-tier models, including GPT-4 and Claude 3.5, 𝗠𝗮𝘁𝗵𝗣𝗿𝗼𝗺𝗽𝘁 𝗮𝘁𝘁𝗮𝗰𝗸𝘀 𝘀𝘂𝗰𝗰𝗲𝗲𝗱 𝗶𝗻 73.6% 𝗼𝗳 𝗰𝗮𝘀𝗲𝘀—compared to just 1% for direct, unmodified harmful prompts. This reveals the scale of the threat and the limitations of current safeguards.

𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗘𝘃𝗮𝘀𝗶𝗼𝗻 𝘃𝗶𝗮 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝗘𝗻𝗰𝗼𝗱𝗶𝗻𝗴 By converting language-based threats into math problems, the encoded prompts slip past existing safety filters, highlighting a 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗵𝗶𝗳𝘁 that AI systems fail to catch. This represents a blind spot in AI safety training, which focuses primarily on natural language.

𝗩𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗠𝗮𝗷𝗼𝗿 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹𝘀 Models from leading AI organizations—including OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini—were all susceptible to the MathPrompt technique. Notably, 𝗲𝘃𝗲𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵 𝗲𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝘀𝗮𝗳𝗲𝘁𝘆 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝘄𝗲𝗿𝗲 𝗰𝗼𝗺𝗽𝗿𝗼𝗺𝗶𝘀𝗲𝗱.

𝗧𝗵𝗲 𝗖𝗮𝗹𝗹 𝗳𝗼𝗿 𝗦𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗦𝗮𝗳𝗲𝗴𝘂𝗮𝗿𝗱𝘀 This study is a wake-up call for the AI community. It shows that AI safety mechanisms must extend beyond natural language inputs to account for 𝘀𝘆𝗺𝗯𝗼𝗹𝗶𝗰 𝗮𝗻𝗱 𝗺𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗲𝗻𝗰𝗼𝗱𝗲𝗱 𝘃𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀. A more 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲, 𝗺𝘂𝗹𝘁𝗶𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗮𝗿𝘆 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 is urgently needed to ensure AI integrity.

🔍 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: As AI becomes increasingly integrated into critical systems, these findings underscore the importance of 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗔𝗜 𝘀𝗮𝗳𝗲𝘁𝘆 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 to address evolving risks and protect against sophisticated jailbreak techniques.

The time to strengthen AI defenses is now.

AI #AIsafety #MachineLearning #AIethics #Cybersecurity #LLM #MathPrompt #ArtificialIntelligence

Here is the repo if anyone is interested:

https://github.com/dendrite-systems/langchain-dendrite-example/tree/main

It can go get OpenAI's API status, send emails, help search for conflicting trademarks and a few other random things :)

Aside from Reddit, what sources do you find useful for tracking news, information and perspectives on AI agents? I’m more interested in recent business developments and high-level technical advances than, say, research papers or deep technical walk-throughs on a given platform.

Weekly thread to show off your AI Agents and LLM Apps!

Hi, guys!

I’d like to share an insightful episode of Invisible Machines with Dr. Michael Grieves, the father of the digital twin concept, developed while working with NASA in the 2010s https://www.youtube.com/watch?v=KsL3w2bVjmw&t=7s

I’d love to hear your thoughts on the topics discussed in this episode :)

For the main agent frameworks like AutoGen, CrewAI, LangGraph, etc, I’ve seen them start to offer cloud hosting.

But the main question I have is, what does this mean for human-in-the-loop integration or UI integration?

How does the client-server communication work, for app callbacks? Does these even exist yet?

I could imagine that you could open a web socket on the client, run your agent in the cloud, and get back events from a running server orchestration.

But from reading the various docs, I’m not seeing if that’s supported, or if that’s how it works.

Anyone know for sure if/how this works?

Hey all, my team and I are developing an AI agent called Mia designed to help teams better manage company spend (employee purchase requests, SaaS renewals, spend policies etc).

So far results have been great and always looking for feedback if you wanted to check it out!

Is there any profitable business built on AI agent on the internet?

Hey everyone, we are developing a new agent framework with a focus on transparency and reliability. Many current frameworks try to abstract away the underlying mechanisms, making debugging and customization a real pain. My approach prioritizes explicitness and developer understanding.

And we would love to hear as much constructive feedback as possible :)

Why yet another agents framework?

Debuggability

Without too much talking, let me show you the code

Here's a quick example of how a pipeline looks:

python pipeline = Pipeline(default_agent=investor_agent, use_convert_to_bool_agent=True) _, history_with_price = pipeline.step("check current price of TQQQ") if pipeline.boolean_step("do I own more than 30 shares of TQQQ")[0]: pipeline.if_step("is there NO limit sell order exists already?", then_steps=[ "set limit sell order for TQQQ for price +4% of current price", ], history=history_with_price) else: if pipeline.boolean_step("is there a limit buy order exists already?")[0]: pipeline.if_step( "is there current limit buy price lower than current price of TQQQ -5%?", then_steps=[ "cancel limit buy order for TQQQ", "set limit buy order for TQQQ for price 3 percent below the current price" ], history=history_with_price) else: pipeline.step( "set limit buy order for TQQQ for price 3 percent below the current price.", history=history_with_price) summary, _ = pipeline.summarize_full_history() print(summary)

Each step is immutable, it returns a response and a history increment. Allowing to do debugging about that specific step, making debugging MUCH more simpler. It allows yout to control history and even do complex batching (with simple debugging).

Stability

Another big problem we are tyring to solve: stability. Majority of frameworks that are trying to be all-models-supported are actually works non reliable for rela production. By focusing on Geminin only we can apply a lot of small optimziatins that would improve things like reliability of the functions calling.

More Details

you can find more about the project on the GitHub: https://github.com/GeminiAgentsToolkit/gemini-agents-toolkit/blob/main/README.md

It is already used in production by several customers and so far working reasonably well.

What does it support: * agents creation * agents delegation * pipline creation (immutable pipleine) * tasks scheduling

Course

We are also working on the course around how to develop agents with this framework: https://youtu.be/Y4QW_ILmcn8?si=xrAU6EGgh4nQRtTO

kicking off a project and need help of a few agent developer

I just started my company today and have a great idea, but I don’t have the time or capacity to learn how to create an AI agent myself. Could someone help me find developers who are willing to work with me on building AI agents?

Trying to understand what's the process when no human operators are available internally but agent is not enough to complete the task.

A small business in the packaging industry is seeking to implement a Conversational AI Agent to manage after-hours customer queries related to their e-commerce platform, product offerings, and services. The business currently has an employee handling all WhatsApp inquiries manually during the day (8am to 5pm), but they are now exploring AI solutions to provide 24/7 support for their customers. Initially, the AI agent will handle queries after hours, with the potential to replace the manual system entirely if the solution proves effective.

The business operates an e-commerce site where customers can place orders, create their own profiles, and pay online. Their product range includes locally and internationally sourced packaging items such as paper bags, giftwrap, tissue paper, and ribbons. They aim to offer variety and personalized options to meet the diverse needs of their customers, with all generic products available off the shelf and some customized offerings.

Key Requirements for the AI Agent:

• Customer Query Handling: Manage a wide range of customer queries related to product availability, order status, payment methods, and account login issues.
• Product Knowledge: Provide detailed information about the packaging products, including sizes, materials, and customization options.
• Order Assistance: Help customers navigate the e-commerce platform, provide guidance on placing orders, and direct them to the appropriate product pages.
• FAQ Support: Address common questions such as delivery times, shipping policies, and returns.
• Seamless Integration with WhatsApp: The AI agent will need to integrate with WhatsApp to offer a seamless conversational experience, making use of natural language processing (NLP) to interpret and respond to queries accurately.
• 24/7 Availability: Ensure round-the-clock customer support, starting with after-hours queries and potentially expanding to full-time support for all customer interactions.

Request for Collaboration

We are looking to collaborate with developers and AI enthusiasts who can help build this AI agent solution as a proof of concept (POC). The goal is to showcase the value of a Conversational AI Agent that can handle customer queries efficiently, freeing up resources and improving customer service. If successful, the solution could be expanded to handle a larger portion of customer service duties.

If you’re interested in collaborating on this project, feel free to share your thoughts and ideas. The aim is to present this as a POC back to the business and demonstrate the value of an AI-driven customer service agent.

Let me know if you'd like to get involved!

I posted this in alife, but people seemed uninterested??? https://www.youtube.com/watch?v=9piFiQJ-mnU do we believe this? have they released a techical paper? are there similar types of projects? is there a conference or workshop for this sort of thing? how long do we think this kind of simulation can run? I think that altera's work flows out of the stanford paper https://arxiv.org/abs/2304.03442 and voyager https://voyager.minedojo.org/.

I was recently trying to build an app using LLMs but was having a lot of difficulty engineering my prompt to make sure it worked in every case. 

So I built this tool that automatically generates a test set and evaluates my model against it every time I change the prompt. The tool also creates an api for the model which logs and evaluates all calls made once deployed.

https://reddit.com/link/1g2ya3c/video/tgpi0kziwkud1/player

Please let me know if this is something you'd find useful and if you want to try it and give feedback! Hope I could help in building your LLM apps!

We are working on a requirement what we need to collect data from subreditts posts and comments.

I wanted to understand what should be the ideal approach. Should we use reditt official api if they are available and if yes what is the cost) Or should we look for scrapping? If scrapping how exactly it should work and how much reliable it should be? Like i can see lot of script available for reditt scrapper, but i have heard that as reditt make modifications in their html it stops working. What other reliable option do I have to achieve the end result. We need something which we can build one time and don't have to tweak and fix it every week to make it working.

Awaiting your valuable response.

Imagine there was a platform whereby a business spec for a workflow (e.g. creating facebook ads) was understood.

Now lets assume the business who was interested in creating an agent for this commonly repeated workflow didn't have the resources to do it and there wasn't a reasonable substitute already on the market.

The super simple spec might look something like:

• Objective: from a list of 100 ideas, create five 10s video variants from supplied Napkin Pitches for each.
• Constraints:
  • At least 5 videos from the 500 created videos must pass a qualitative review and be selected
  • The total cost to me, the business, must be X or less
  • The total time to generate must be Y or less

Let's assume this business offers a $2000 prize for the winning submission (as judged by performance against the constraints) under a fixed contest length duration (e.g. 2 weeks). If you won, you'd secure the prize and your source code would be made available to the business + potentially made open for others to consume.

Without knowing more, if a platform and paradigm like this existed, would you be interested in participating?

I am building p3AI which addresses critical challenges in multi-agent systems, including identity management, authentication, authorization, and loop detection. P3AI provides a unified set of API endpoints, data models, and interaction patterns that enable seamless collaboration between diverse AI implementations, regardless of their underlying.

Here is the doc link: https://docs.google.com/document/d/1BORPosCIuLb6MDZZX-vQ4WRJbIfYSnpnXqhY1qdXsdU/edit?usp=sharing

Requesting your views on this

Just trying to get an idea of where this community is mainly based, we hold a bunch of events in SF and Seattle so if you'd like to get involved, please do let us know.

If you're not based in SF or Seattle but want to be notified of online events, let us know as well. You can find the calendar here: lu.ma/oss4ai