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AIOS: Operating System for LLM Agents

New Post has been published on https://thedigitalinsider.com/aios-operating-system-for-llm-agents/

AIOS: Operating System for LLM Agents

Over the past six decades, operating systems have evolved progressively, advancing from basic systems to the complex and interactive operating systems that power today’s devices. Initially, operating systems served as a bridge between the binary functionality of computer hardware, such as gate manipulation, and user-level tasks. Over the years, however, they have developed from simple batch job processing systems to more sophisticated process management techniques, including multitasking and time-sharing. These advancements have enabled modern operating systems to manage a wide array of complex tasks. The introduction of graphical user interfaces (GUIs) like Windows and MacOS has made modern operating systems more user-friendly and interactive, while also expanding the OS ecosystem with runtime libraries and a comprehensive suite of developer tools.

Recent innovations include the integration and deployment of Large Language Models (LLMs), which have revolutionized various industries by unlocking new possibilities. More recently, LLM-based intelligent agents have shown remarkable capabilities, achieving human-like performance on a broad range of tasks. However, these agents are still in the early stages of development, and current techniques face several challenges that affect their efficiency and effectiveness. Common issues include the sub-optimal scheduling of agent requests over the large language model, complexities in integrating agents with different specializations, and maintaining context during interactions between the LLM and the agent. The rapid development and increasing complexity of LLM-based agents often lead to bottlenecks and sub-optimal resource use.

To address these challenges, this article will discuss AIOS, an LLM agent operating system designed to integrate large language models as the ‘brain’ of the operating system, effectively giving it a ‘soul.’ Specifically, the AIOS framework aims to facilitate context switching across agents, optimize resource allocation, provide tool services for agents, maintain access control, and enable concurrent execution of agents. We will delve deep into the AIOS framework, exploring its mechanisms, methodology, and architecture, and compare it with state-of-the-art frameworks. Let’s dive in.

After achieving remarkable success in large language models, the next focus of the AI and ML industry is to develop autonomous AI agents that can operate independently, make decisions on their own, and perform tasks with minimal or no human interventions. These AI-based intelligent agents are designed to understand human instructions, process information, make decisions, and take appropriate actions to achieve an autonomous state, with the advent and development of large language models bringing new possibilities to the development of these autonomous agents. Current LLM frameworks including DALL-E, GPT, and more have shown remarkable abilities to understand human instructions, reasoning and problem solving abilities, and interacting with human users along with external environments. Built on top of these powerful and capable large language models, LLM-based agents have strong task fulfillment abilities in diverse environments ranging from virtual assistants, to more complex and sophisticated systems involving creating problem solving, reasoning, planning, and execution. 

The above figure gives a compelling example of how an LLM-based autonomous agent can solve real-world tasks. The user requests the system for a trip information following which, the travel agent breaks down the task into executable steps. Then the agent carries out the steps sequentially, booking flights, reserving hotels, processing payments, and more. While executing the steps, what sets these agents apart from traditional software applications is the ability of the agents to show decision making capabilities, and incorporate reasoning in the execution of the steps. Along with an exponential growth in the quality of these autonomous agents, the strain on the functionalities of large language models, and operating systems has witnessed an increase, and an example of the same is that prioritizing and scheduling agent requests in limited large language models poses a significant challenge. Furthermore, since the generation process of large language models becomes a time consuming task when dealing with lengthy contexts, it is possible for the scheduler to suspend the resulting generation, raising a problem of devising a mechanism to snapshot the current generation result of the language model. As a result of this, pause/resume behavior is enabled when the large language model has not finalized the response generation for the current request. 

To address the challenges mentioned above, AIOS, a large language model operating system provides aggregations and module isolation of LLM and OS functionalities. The AIOS framework proposes an LLM-specific kernel design in an attempt to avoid potential conflicts arising between tasks associated and not associated with the large language model. The proposed kernel segregates the operating system like duties, especially the ones that oversee the LLM agents, development toolkits, and their corresponding resources. As a result of this segregation, the LLM kernel attempts to enhance the coordination and management of activities related to LLMs. 

AIOS : Methodology and Architecture

As you can observe, there are six major mechanisms involved in the working of the AIOS framework. 

Agent Scheduler: The task assigned to the agent scheduler is to schedule and prioritize agent requests in an attempt to optimize the utilization of the large language model. 

Context Manager: The task assigned to the context manager is to support snapshots along with restoring the intermediate generation status in the large language model, and the context window management of the large language model. 

Memory Manager: The primary responsibility of the memory manager is to provide short term memory for the interaction log for each agent. 

Storage Manager: The storage manager is responsible to persist the interaction logs of agents to long-term storage for future retrieval. 

Tool Manager: The tool manager mechanism manages the call of agents to external API tools. 

Access Manager: The access manager enforces privacy and access control policies between agents. 

In addition to the above mentioned mechanisms, the AIOS framework features a layered architecture, and is split into three distinct layers: the application layer, the kernel layer, and the hardware layer. The layered architecture implemented by the AIOS framework ensures the responsibilities are distributed evenly across the system, and the higher layers abstract the complexities of the layers below them, allowing for interactions using specific modules or interfaces, enhancing the modularity, and simplifying system interactions between the layers. 

Starting off with the application layer, this layer is used for developing and deploying application agents like math or travel agents. In the application layer, the AIOS framework provides the AIOS software development kit (AIOS SDK) with a higher abstraction of system calls that simplifies the development process for agent developers. The software development kit offered by AIOS offers a rich toolkit to facilitate the development of agent applications by abstracting away the complexities of the lower-level system functions, allowing developers to focus on functionalities and essential logic of their agents, resulting in a more efficient development process. 

Moving on, the kernel layer is further divided into two components: the LLM kernel, and the OS kernel. Both the OS kernel and the LLM kernel serve the unique requirements of LLM-specific and non LLM operations, with the distinction allowing the LLM kernel to focus on large language model specific tasks including agent scheduling and context management, activities that are essential for handling activities related to large language models. The AIOS framework concentrates primarily on enhancing the large language model kernel without alternating the structure of the existing OS kernel significantly. The LLM kernel comes equipped with several key modules including the agent scheduler, memory manager, context manager, storage manager, access manager, tool manager, and the LLM system call interface. The components within the kernel layer are designed in an attempt to address the diverse execution needs of agent applications, ensuring effective execution and management within the AIOS framework. 

Finally, we have the hardware layer that comprises the physical components of the system including the GPU, CPU, peripheral devices, disk, and memory. It is essential to understand that the system of the LLM kernels cannot interact with the hardware directly, and these calls interface with the system calls of the operating system that in turn manage the hardware resources. This indirect interaction between the LLM karnel’s system and the hardware resources creates a layer of security and abstraction, allowing the LLM kernel to leverage the capabilities of hardware resources without requiring the management of hardware directly, facilitating the maintenance of the integrity and efficiency of the system. 

Implementation

As mentioned above, there are six major mechanisms involved in the working of the AIOS framework. The agent scheduler is designed in a way that it is able to manage agent requests in an efficient manner, and has several execution steps contrary to a traditional sequential execution paradigm in which the agent processes the tasks in a linear manner with the steps from the same agent being processed first before moving on to the next agent, resulting in increased waiting times for tasks appearing later in the execution sequence. The agent scheduler employs strategies like Round Robin, First In First Out, and other scheduling algorithms to optimize the process. 

The context manager has been designed in a way that it is responsible for managing the context provided to the large language model, and the generation process given the certain context. The context manager involves two crucial components: context snapshot and restoration, and context window management. The context snapshot and restoration mechanism offered by the AIOS framework helps in mitigating situations where the scheduler suspends the agent requests as demonstrated in the following figure. 

As demonstrated in the following figure, it is the responsibility of the memory manager to manage short-term memory within an agent’s lifecycle, and ensures the data is stored and accessible only when the agent is active, either during runtime or when the agent is waiting for execution. 

On the other hand, the storage manager is responsible for preserving the data in the long run, and it oversees the storage of information that needs to be retained for an indefinite period of time, beyond the activity lifespan of an individual agent. The AISO framework achieves permanent storage using a variety of durable mediums including cloud-based solutions, databases, and local files, ensuring data availability and integrity. Furthermore, in the AISO framework, it is the tool manager that manages a varying array of API tools that enhance the functionality of the large language models, and the following table summarizes how the tool manager integrates commonly used tools from various resources, and classifies them into different categories. 

The access manager organizes access control operations within distinct agents by administering a dedicated privilege group for each agent, and denies an agent access to its resources if they are excluded from the agent’s privilege group. Additionally, the access manager is also responsible to compile and maintain auditing logs that enhances the transparency of the system further. 

AIOS : Experiments and Results

The evaluation of the AIOS framework is guided by two research questions: first, how is the performance of AIOS scheduling in improving balance waiting and turnaround time, and second, whether the response of the LLM to agent requests are consistent after agent suspension?

To answer the consistency questions, developers run each of the three agents individually, and subsequently, execute these agents in parallel, and attempt to capture their outputs during each stage. As demonstrated in the following table, the BERT and BLEU scores achieve the value of 1.0, indicating a perfect alignment between the outputs generated in single-agent and multi-agent configurations. 

To answer the efficiency questions, the developers conduct a comparative analysis between the AIOS framework employing FIFO or First In First Out scheduling, and a non scheduled approach, wherein the agents run concurrently. In the non-scheduled setting, the agents are executed in a predefined sequential order: Math agent, Narrating agent, and rec agent. To assess the temporal efficiency, the AIOS framework employs two metrics: waiting time, and turnaround time, and since the agents send multiple requests to the large language model, the waiting time and the turnaround time for individual agents is calculated as the average of the waiting time and turnaround time for all the requests. As demonstrated in the following table, the non-scheduled approach displays satisfactory performance for agents earlier in the sequence, but suffers from extended waiting and turnaround times for agents later in the sequence. On the other hand, the scheduling approach implemented by the AIOS framework  regulates both the waiting and turnaround times effectively. 

Final Thoughts

In this article we have talked about AIOS, an LLM agent operating system that is designed in an attempt to embed large language models into the OS as the brain of the OS, enabling an operating system with a soul. To be more specific, the AIOS framework is designed with the intention to facilitate context switching across agents, optimize resource allocation, provide tool service for agents, maintain access control for agents, and enable concurrent execution of agents. The AISO architecture demonstrates the potential to facilitate the development and deployment of large language model based autonomous agents, resulting in a more effective, cohesive, and efficient AIOS-Agent ecosystem. 

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Era by Era: The Advancement of AI Agents

The advent of Generative AI has sparked a wave of enthusiasm among businesses eager to harness its potential for creating Chatbots, companions, and copilots designed to unlock insights from vast datasets. This journey often begins with the art of prompt engineering, which presents itself in various forms, including Single-shot, Few-shot, and Chain of Thought methodologies. Initially, companies tend to deploy internal chatbots to bolster employee productivity by facilitating access to critical insights. Furthermore, customer support, traditionally seen as a cost center, has become a focal point for optimization efforts, leading to the development of Retrieval Augmented Generation (RAG) systems intended to provide deeper insights. However, challenges such as potential inaccuracies or "hallucinations" in responses generated by these RAG systems can significantly impact customer service representatives' decision-making, potentially resulting in customer dissatisfaction. A notable incident involving Air Canada has recently highlighted the potential risks to brand reputation and financial stability posed by deploying these autonomous chatbots in customer support scenarios. The prospect of creating similar chatbots for financial advisors, capable of delivering human-like yet fundamentally flawed responses, raises significant concerns. Issues related to quality (such as hallucination, truth grounding, and comprehensiveness), content safety, and the risk of intellectual property leakage are among the key hurdles preventing many generative AI applications from reaching production stages.

Challenges in achieving quality and trust

It is easy to build a simple RAG system by combining Vector search for retrieval and LLM to summarize retrieved chunks, a massive upgrade from traditional knowledge bases with a limited understanding of the semantic nature of questions. These systems show poor performance in the real world for a multipart of complex questions.

Let's deep dive into the challenges by breaking down the RAG system,

Question semantics: Complex queries often encompass multipart intents that may be unrelated or even adversarial, designed to confuse the model or "jailbreak" the chatbot. These can range from greetings to questions that test the system's limitations or probe for inconsistencies. Without understanding these nuances, a RAG system might fail to appropriately categorize and respond to the query, leading to irrelevant or incorrect answers.

Retrieval phase: A single vector store search may not yield relevant results for complex or multipart statistical questions. Personalized queries, such as those asking for specific information about a user's insurance policy, pose additional challenges if the system needs access to personalized data points like the policies owned by the user. This limitation can prevent the system from providing accurate, user-specific information.

Prompt augmentation: In simpler RAG implementations, the system prompt is static, combined with retrieved contextual information to create an augmented prompt. This static nature can limit the system's ability to dynamically adjust to the specifics of the query, particularly for complex or evolving scenarios that require a more nuanced understanding and response.

LLM for Summarization: If the augmented prompt lacks the necessary context to answer the query effectively, LLMs may rely on their inherent knowledge base to fill in the gaps, leading to "hallucination," where the model generates plausible but inaccurate or fabricated information. This issue is particularly problematic in scenarios requiring precise, factual responses.

Rise of Agents

Prompt engineering techniques such as Chain of Thoughts (CoT) involve generating intermediate steps or reasoning paths when solving complex problems, especially in language models. It's like showing one's work in math problems but applied to AI. The model explicitly generates a sequence of thoughts or reasoning steps before arriving at a final answer or conclusion. Although CoT excels at breaking down complex tasks or questions, their effectiveness hinges on the context provided if used in RAG systems.

The ReACT (Synergizing Reasoning and Acting in Language Models) paper shows how this approach is far superior to CoTs. Let's look into the basics. In the study of autonomous agents and multi-agent systems, the concepts of Thought, Action, and Observation play crucial roles in defining how these agents perceive, interpret, and interact with their environment.

Thought in AI agents refers to the internal processing or decision-making mechanisms that occur before taking an action. It involves the interpretation of observations, the weighing of possible actions based on learned experiences or predefined rules, and the formulation of a plan or response. Thought processes in AI can range from simple if-then rules to complex algorithms that involve reasoning, planning, and prediction based on deep learning models.

Action is the step an AI agent takes in response to its thoughts and observations. It's the execution phase where the agent applies its decision to the environment, potentially altering its state. Actions can be physical movements, such as a robotic arm picking up an object, or digital responses, like sending a message or updating a database. The scope of actions available to an AI agent depends on its capabilities and the effectors it has to interact with its environment.

Observation involves the agent's perception of its environment through sensors or input mechanisms. It can include data from visual cameras, microphones, temperature sensors, or digital inputs like API calls. Observations are the raw data that an AI agent receives and processes to understand its current context or the state of the environment. Effective observation is critical for an agent to make informed decisions and adapt actions accordingly.

Together, Thought, Action, and Observation form a cyclical process that enables AI agents to operate autonomously, learn from their environment, and achieve their goals.

RAG Agents

Agentic workflows, also known as Agents, harness the capabilities of Large Language Models (LLMs) to navigate the complexities of constructing intricate Retrieval Augmented Generation (RAG) systems. They adeptly segment elaborate tasks into manageable sub-tasks, utilize external systems to enhance their knowledge base, and monitor the outcomes to determine subsequent actions, ensuring the initial query's goals are met. The following provides a standard depiction of how a RAG system incorporates external resources for knowledge expansion.

There are several providers of Agentic solutions,

Langchain implements ReACT and several simple tutorials for customer service, Text 2 SQL and code interpreter.

LlamaIndex provides its agentic implementation using ReACT and OpenAI

OpenAI also introduced GPTs to create custom versions of ChatGPT by combining instructions, external knowledge, and combination of skills

Amazon Bedrock Agents allows you to build and configure autonomous agents in your application. An agent helps end-users complete actions based on organization data and user input. Agents orchestrate interactions between foundation models (FMs), data sources, software applications, and user conversations.

Semantic Kernel is an open-source project developed by Microsoft. It is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code.

Numerous options exist for creating Agentic workflows, yet they are not without challenges, including potential loops from unclear prompts or Large Language Models (LLMs) errors. Karini AI streamlines the process, enabling the rapid development and deployment of production-grade agentic workflows with the following features:

Pre-built prompts: Get a head start with a comprehensive library of Agentic Prompt templates designed for various needs like customer service, HR, IT, legal, and finance. These templates save you valuable time and effort.

Experiment and Refine: Seamlessly connect external tools to your workflow, enhancing your prompt creation process. Design compelling prompts and engage in interactive testing sessions with your AI agents. Analyze outcomes from top model providers and log your findings to identify best practices.

Rapid Deployment: Recipes for RAGs (Retrieval Augmented Generation) expedite the deployment of your AI workflows, complete with integrated performance, usage, and cost monitoring.

Deploy with Confidence: Integrate an agentic co-pilot directly into your systems. Choose from optional safety features for added peace of mind.

Recipes for RAGs: expedite the deployment of agentic workflows, complete with integrated performance, usage, and cost monitoring.Create custom greetings to enhance user experience. Continuously improve your AI with a built-in feedback mechanism.

Karini AI empowers you to build, deploy, and manage powerful AI agents efficiently. Start your journey today!

Conclusion:

The ReAct agent represents an advanced form of artificial intelligence, drawing inspiration from the human processes of thinking, acting, and observing to tackle challenges methodically. Whether you're a Generative AI aficionado or looking to gain a competitive edge by creating production-level agents through an intuitive visual platform, the Karini AI platform is designed to accelerate your journey to market with ethical AI solutions.

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In the high-speed world of business, staying ahead of technological advances is crucial for success. For franchise owners and small business operators, this is particularly pressing. Dialing Innovations, a company at the vanguard of telecommunication solutions, has recently announced plans that could significantly change the game: the deployment of localized AI tailored to specific business…

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A query to Copilot: “Name some AI agent startups.”

Copilot’s answer: Some AI agent startups are:

- **AutoGPT**: An experimental open-source application that uses GPT-4 to chain together LLM "thoughts" and autonomously achieve any goal. It raised $12 million in funding and is developing a GUI for its platform. ¹

- **Relevance AI**: A no-code AI agent builder that allows users to train agents with natural language and connect them with other systems and agents. It raised $18 million in funding and is used by Fortune 500s and startups. ¹

- **LangChain**: A startup that leverages LLMs to create decentralized AI agents that can translate any language and content. It raised $10 million in funding from Benchmark and is one of the fastest growing GitHub projects. ¹

- **Cohere**: A startup that builds natural language understanding models and APIs for AI agents. It raised $40 million in funding and is backed by OpenAI and Shopify. ¹

- **Second**: A startup that creates AI agents that can write high-quality content for any domain and purpose. It raised $15 million in funding and is used by publishers and marketers. ²

- **Lindy.ai**: A startup that develops AI agents that can generate and optimize code for any programming language and framework. It raised $20 million in funding and is used by developers and enterprises. ²

- **Spell.so**: A startup that creates AI agents that can proofread and improve any text with natural language suggestions. It raised $8 million in funding and is used by students and professionals. ²

- **Fixie.ai**: A startup that builds AI agents that can diagnose and repair any software bug or issue. It raised $12 million in funding and is used by engineers and support teams. ²

Source: Conversation with Bing, 1/15/2024

(1) AI Agent Startups to Watch. https://www.mlq.ai/ai-agent-startups-to-watch/.

(2) The Rise of Autonomous AI Agents: Debundling the Market Landscape. https://www.aura.vc/articles/the-rise-of-autonomous-ai-agents-debundling-the-market-landscape.

(3) Best AI Agents for Startups - 2024 Reviews & Comparison - SourceForge. https://sourceforge.net/software/ai-agents/for-startup/.

(4) 50 Generative AI Startups to Watch This Year - eWeek. https://www.eweek.com/news/generative-ai-startups/.

AI Pixel Studio Review, Features, Pricing, OTO, Demo + Special Discount(2024)

AI Pixel Studio Introduction

Welcome My Review Of AI Pixel Studio is a cutting-edge cloud-based tool that uses artificial intelligence to create stunning films, graphics, and photos with never-before-seen skill. It may be used with text input, voice commands, or freehand sketching. Its layout is a true asset to the fields of content creators, independent contractors, marketing virtuosos, and visionaries, enabling them to produce exceptional material that not only engages viewers deeply but also captivates them.

AI Pixel Studio is more than just a text-to-video transmogrifier or a simple pixel art generator. All in all, it’s a comprehensive AI system ready to take on a wide range of content creation tasks, with countless possibilities:

AI Pixel Studio Overview

Vendor: Vivek Gour Product: AiPixel Studio Launch Date: 2023-Aug-01 Launch Time: 11:00 EDT Front-End Price: $47 Product Page:Click Here Niche: Software

Refund :14 Day Guarantee

What is AiPixel Studio?

AiPixel Studio In a world where time is of the essence, our search for effective answers to common problems has brought artificial intelligence closer to hand. Artificial Intelligence (AI) is a cutting-edge technology that is revolutionizing work and productivity. Artificial intelligence’s remarkable powers have opened up previously unheard-of levels of efficiency, simplifying procedures that earlier required a substantial investment of time and money.

Metamorphose mundane sketches into hyper-realistic AI masterpieces, infusing life into the ordinary.

Craft AI-rendered cartoon imagery and graphics, manifesting whimsical and charismatic visual narratives.

Meticulously extract backgrounds from images, facilitating the creation of visually striking compositions.

Conjure mesmerizing 4K high-definition videos from mere textual input, a testament to the transformative potential of AI creativity.

Reshape the age portrayal within any image, crafting aesthetically pleasing transformations, an artistic metamorphosis at your fingertips.

Forge 4K AI videos, a catalyst for an exponential surge in conversion rates, a harbinger of marketing success.

Embark on the creation of multi-subject images without incurring exorbitant costs, democratizing multi-faceted content creation.

Resurrect old photographs, imbuing them with renewed vitality through the magic of AI reanimation, a poignant journey down memory lane.

Effortlessly dispel the obfuscating shroud of blur from images, instantly restoring clarity, an antidote to visual ambiguity.

AiPixel Studio Explore the Features And Pricing:

Are you prepared to use Ai Studio to elevate your artistic endeavors to new heights? With the least amount of work, you can accomplish remarkable achievements thanks to this amazing program. Let’s explore the amazing capabilities that this tool has to offer:

Convert Normal Sketch Images Into AI Real Images Transform ordinary sketches into stunning, realistic AI images with ease.

Remove Backgrounds From Images: Effortlessly achieve clean, professional results by removing backgrounds from images.

Convert Any Drawing Into Stunning AI Artworks: Turn any drawing into mesmerizing AI artworks that leave a lasting impression.

Craft AI Cartoon Images And Graphics: Unleash your imagination and effortlessly create captivating AI cartoon images and graphics.

Built-In AI Image Translation: Seamlessly translate images with the help of built-in AI technology.

AI 4K Videos:

Harness the power of AI to produce Ultra HD videos for any niche and attract a maximum number of customers.

More Deatils

AI-Powered Productivity with Taskade

Taskade is an AI-powered productivity platform that offers a range of tools to help teams work more efficiently. The platform includes five AI-powered tools in one to supercharge your team productivity. With Taskade, all your work is in sync in one unified workspace.

Automate 700+ Tasks with AI Agents

Boost productivity with our Custom AI Agents. Experience the future as you build, train, and deploy your AI workforce. Accomplish tasks at 10x speed, powered by our AI chatbot, project assistant, workflow generator, and more.

Generate Dynamic Workflows with AI

Spark creativity with a task or objective. Generate dynamic to-do lists, flow charts, project sprints, SOPs, and more. Visualize your work in multiple dimensions—lists, boards, tables, calendars, mind maps, and more. Streamline with AI and bring your vision to life.

Chat with AI

Bring your projects to life with an AI assistant designed for brainstorming and task coordination. Chat with your tasks and documents, and choose a persona tailored to various roles and expertise. Taskade AI is ready to assist you right inside your projects.

Visualize Notes

Embrace a smart, structured outlining experience, mirroring your brain’s natural organization. Create infinite connections and levels of hierarchy, with real-time syncing.

Turn Ideas into Actions with AI

Harness AI to generate new ideas and map out anything. Convert your brainstorming sessions into mind maps and track progress across projects. Taskade is your creative canvas for dynamic workflows, like the art of origami.

Taskade is available on Android, iOS, Mac, Windows, and Linux. The platform offers a seamless experience across all devices. You can share projects for review, invite others into your workspace, or hop on a real-time chat and video call with stakeholders anywhere. Taskade also provides unlimited sharing, team collaboration, and video chat & meetings.

Taskade is a great tool for teams looking to streamline their workflow and increase productivity. By using Taskade, you can automate tasks, generate dynamic workflows, chat with AI, visualize notes, and turn ideas into actions. With its AI-powered features, Taskade is a great way to supercharge your team productivity. I hope this helps you write your blog post!

Composio proudly introduces its groundbreaking GPT function calling feature, a game-changer in the realm of artificial intelligence and automation. With GPT function calling, developers can seamlessly integrate the power of OpenAI's GPT models into their applications, unlocking a world of possibilities for natural language processing, content generation, and intelligent automation. Our platform simplifies the process of implementing GPT function calling into existing workflows, offering intuitive interfaces and robust APIs that empower developers to harness the full potential of AI with ease. Whether you're building chatbots, virtual assistants, or data analysis tools, Composio's GPT function calling feature enables you to leverage state-of-the-art language models to enhance your applications' capabilities and deliver unparalleled user experiences.

https://blog.composio.dev/gpt-4-function-calling-example/

Beyond Expectations: AI Agents and the Next Chapter of Work

New Post has been published on https://thedigitalinsider.com/beyond-expectations-ai-agents-and-the-next-chapter-of-work/

Beyond Expectations: AI Agents and the Next Chapter of Work

AI agents, or autonomous agents, are in their early days. Very early – the bottom of the first inning early. The field is buzzing with innovation, from groundbreaking research to proof of concepts to practical applications – all hinting at AI’s vast potential. 

There is no doubt that autonomous agents will transform every single industry, with their capabilities extending beyond mere task automation to redesigning workflows, simulating complex scenarios, and reducing the need for human intervention in various processes. We’re looking at a (near-term) future where agents can run large-scale simulations, redesign marketing campaigns, or even automate complex R&D testing processes.

Boston Consulting Group (BCG) highlights the evolutionary leap from large language models (LLMs) to autonomous agents designed to execute tasks end-to-end, monitor outcomes, adapt, and use tools autonomously to achieve goals. They represent a significant step towards true artificial intelligence, capable of independent operation without continuous human oversight. 

In terms of market size, autonomous AI and autonomous agents were valued at 4.8 billion USD in 2023 and are estimated to register a CAGR of over 43% between 2023 and 2028, reaching 28.5 billion. It’s clear that we’re on the cusp of a paradigm shift – a phase filled with anticipation, excitement, skepticism, and pragmatic evaluation. This shift isn’t just about technological advancement; it’s about redefining our very approach to work, productivity, and innovation. Nearly every investor, founder, developer, and tech enthusiast is trying to understand the impact this technology will have on how we work in our lifetime and beyond, and assess the implications for their operations and strategic goals. 

However, as of now, we lack the capability to fully comprehend the magnitude of the mass shift this will cause. All we can do is speculate. This article is just that – my speculation about the unfolding dynamics of autonomous agents and its implications for founders, investors, and the broader economy. I’ll talk about how we at Forum Ventures are thinking about and investing in the space, as well as provide a market map with the companies we believe are leading the exploration. 

Where We Are At Today

Despite the considerable advancements in research and proof of concepts, we’re all still trying to make sense of and project out how to harness the full capabilities of AI agents. So far, there is a confluence of three trends:

Advancements in AI proficiency and efficiency, expanding the boundaries of what’s possible. 

The decreasing cost of actioning capabilities, such as ChatGPT 4.0, for example, making the use of AI agents more accessible to more people and causing wider adoption and the overall embracing of this technology.

The democratization of access to AI, open source or not, enabling a wider range of entities to explore and implement AI solutions, thereby accelerating the pace of innovation.

As with any new technology, especially a transformation as big as this, there are an array of challenges that are in the process of being addressed. Here are the top two:

1. Safety & Accuracy

There’s a growing focus on developing the necessary infrastructure to ensure the safe and ethical deployment of AI agents. For many industries and businesses, there is no room for error. If an LLM has a hallucination rate of even just 0.1% it could never be trusted in any critical process, and this error rate needs to be even lower for a 10 step or 100 step process. Solving this is paramount to widespread adoption, and many companies are waiting before they embrace LLMs either as part of their tech stack or as an entirely new way of operating. 

Tools for monitoring accuracy and safety through observability and user permissioning, as well as ethical frameworks, are being established to foster a responsible approach to AI integration. We’ve seen some companies doing this well, PrivateAI being one of them. They use inference to make sure companies are not training on private data so that it doesn’t leak. We’re also very excited about new companies coming to market like SafeguardAI – an autonomous AI agent that safeguards for hallucinations, allowing enterprises to deploy generative AI use faster.

Additionally, tools like automatic evaluation metrics, human evaluation frameworks, and diagnostic datasets are being developed to assist in the assessment and improvement of LLMs’ accuracy. These tools help researchers and developers identify strengths and weaknesses in LLMs and guide further advancements in the field.

2. Human-AI Interaction

The challenge here is to what extent should humans interact with software that’s autonomous. There are concerns about the potential risks of AI systems operating without sufficient human control, i.e. how much autonomy is too much. But we also need to figure out how much we want humans in the loop, and what level of human interaction creates more safety whilst limiting biases and decreasing the chance for human error. We don’t have good answers to this yet, at any sort of reasonable scale.

From an opportunist perspective, I’m hopeful we can define a new paradigm for autonomous software to operate inside the control of humans in a way that it’s being monitored and observed so that humans can stop potentially “fatal” things from happening like a much bigger version of a flash crash in the economy. In my opinion, those who can build this will win and deliver transformational opportunities. 

The Shift from Task-Oriented to Goal-Oriented Processes

There isn’t going to be any sector or field of work that will remain untouched by AI agents, and a lot of the change that happens will be in the near future. In my opinion, one of the most profound impacts that AI agents will have is the shift from task-oriented to goal-oriented processes. Today, you input something into a computer, such as “write me an op-ed about AI Agents”, and the computer gives something back to you, which you then action. This is a very task-oriented prompt, and still requires the user to train the agent according to the goals and tone of voice of the person. However, it is limited to this, and therefore the output is largely determined by the quality of the training input, plus the pre-determined (and possibly limited) goals of the user, which is still heavily reliant on human actions. 

The underutilized power of AI agents is in the power of goal-oriented work. The future will no longer be one of rote step by step process description or complicated prompt engineering for processes. Companies and leaders should shift their thinking of how they build and use autonomous rules-based processes, whereby goals are prescribed and agents determine the best path forward to achieve that outcome (with appropriate human interventions). An example of this could be, “book me an event in New York City with 100 professionals that want to learn about how AI is penetrating the U.S. healthcare market from one of our speakers”. In a case like this, AI will be utilized to operationalize strategic thinking beyond the limited scope of possibility that a simple task could accomplish.

This is a whole new way of thinking and working. There are almost no set of goals we are currently pursuing with a computer that won’t be pursued wildly differently. This will be a fundamental change in how we orient ourselves, and how work is conceived and executed. 

Monetization and Market Dynamics

As AI becomes more integral to business models, traditional monetization strategies are being re-evaluated. For example, right now in enterprise software, generally, customers buy seats and usage. On the consumer side, people make in-app purchases. Our hypothesis is that this will shift such that increasingly, software companies will be able to sell outcomes, rather than tools. Will people and businesses pay for results? For their goals to be reached? We’re not sure yet. But we see this as a reflection of the broader trend towards value-based engagements. However, there are challenges in predicting profitability and managing costs, especially given the computationally intensive nature of AI technologies. 

Deciding Who And What To Invest In At The Earliest Stage

Whenever we’re investing at this early stage, the founder is one of the biggest bets we make  – looking at both founder-market fit and founder personality. With AI Agents, this lens becomes even more important because with so many unknowns, the solution being built today will likely not be what’s being built tomorrow, but the founder will stay the same. So, we look at not only founder-market fit, but also their attachment to the problem, how they look at the problem set differently than the existing paradigm, that they are willing to embrace the unknown, and that they have plasticity and flexibility to keep pace with a market that has this much flux. 

After the founder, we look at the market and if there is a large total addressable market and a credible path to a $1B revenue opportunity. We are open to both legacy markets like proptech and supply chain, and more forward thinking, flexible markets like fintech and eCommerce, as long as the startup solution / tool will deliver a step function improvement over the old way.

Our third focus when evaluating an AI agent solution is if the tool will be compatible within an AI-centric software future. In other words, will the proposed solution seamlessly integrate with and enhance how we see the future software landscape and stack within that market.

We can’t make proper cost-based predictions yet. Right now, AI businesses are fundamentally less profitable than SaaS businesses. The costs associated with processing and analyzing data in AI systems can quickly accumulate. There will need to be near-term progress that enhances AI efficiency and reduces operational costs before we can do this type of evaluation. Ideally, there are advancements that mirror Moore’s Law in the AI sector, and both power and chip costs are reduced due to increased investments. If we can find a balance where AI is not only innovative but also economically sustainable, then we’re golden. But there are still so many unknowns, and most of us are guessing (making informed speculations, to put it nicely).

A ‘Brave New World’ of Possibilities

Most people consider the introduction of ChatGPT to be AI’s”iPhone moment”. However, I don’t think we’re there…yet. To date, these chat interfaces haven’t done much more than streamline our current workflows. While these tools have undoubtedly made tasks easier to manage, our approach remains fundamentally task-oriented. The broader vision is to transform this dynamic entirely, where AI will be able to operationalize strategic thinking and perform complex output, with even less input from humans. The true iPhone moment, therefore, might be the unveiling of AI Agents as the default B2B application set, which will in turn have an outsized impact on the future of work. 

A decade from now, there is no doubt that we’re going to look back and marvel at the idea that we used to operate based on to-do lists rather than setting strategic goals and allowing AI to help us iterate and refine those objectives. This shift toward a goal-oriented work environment represents not just an evolution in technology but a transformation in how we conceptualize and approach our work. 

The path forward is filled with uncertainties, but the potential for AI to revolutionize industries, amplify human potential, drive meaningful progress, and deliver lasting value is undeniable. Our commitment is to navigate these uncertainties, and identify, bet on, and support early-stage AI initiatives and the brilliant minds that are bringing their visions to life.