How gen AI–enabled agents could work Agents can support high-complexity use cases across industries and business functions, particularly for workflows involving time-consuming tasks or requiring various specialized types of qualitative and quantitative analysis. Agents do this by recursively breaking down complex workflows and performing subtasks across specialized instructions and data sources to reach the desired goal. The process generally follows these four steps: User provides instruction: A user interacts with the AI system by giving a natural-language prompt, much like one would instruct a trusted employee. The system identifies the intended use case, asking the user for additional clarification when required. Agent system plans, allocates, and executes work: The agent system processes the prompt into a workflow, breaking it down into tasks and subtasks, which a manager subagent assigns to other specialized subagents. These subagents, equipped with necessary domain knowledge and tools, draw on prior “experiences” and codified domain expertise, coordinating with each other and using organizational data and systems to execute these assignments. Agent system iteratively improves output: Throughout the process, the agent may request additional user input to ensure accuracy and relevance. The process may conclude with the agent providing final output to the user, iterating on any feedback shared by the user. Agent executes action: The agent executes any necessary actions in the world to fully complete the user-requested task https://lnkd.in/eNr7GH7K
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How gen AI–enabled agents could work Agents can support high-complexity use cases across industries and business functions, particularly for workflows involving time-consuming tasks or requiring various specialized types of qualitative and quantitative analysis. Agents do this by recursively breaking down complex workflows and performing subtasks across specialized instructions and data sources to reach the desired goal. The process generally follows these four steps: - User provides instruction: A user interacts with the AI system by giving a natural-language prompt, much like one would instruct a trusted employee. The system identifies the intended use case, asking the user for additional clarification when required. - Agent system plans, allocates, and executes work: The agent system processes the prompt into a workflow, breaking it down into tasks and subtasks, which a manager subagent assigns to other specialized subagents. These subagents, equipped with necessary domain knowledge and tools, draw on prior “experiences” and codified domain expertise, coordinating with each other and using organizational data and systems to execute these assignments. - Agent system iteratively improves output: Throughout the process, the agent may request additional user input to ensure accuracy and relevance. The process may conclude with the agent providing final output to the user, iterating on any feedback shared by the user. - Agent executes action: The agent executes any necessary actions in the world to fully complete the user-requested task. https://lnkd.in/eCbjpKY5
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Introducing our AI Associate for VDR analyses: Upload any spreadsheet, and we'll deliver a 25+ page PPT summary, in your firm’s formatting. We’ll always answer standard diligence questions regarding growth, margins, performance by cohorts, retention, etc. We’ll even tackle more complex questions like “How do the top customers perform relative to the rest of the customer base?” Of course, you can come in and guide the Associate with your custom questions as well. This is such a painful workflow that some PE funds outsource this to Big 4 Consultants at $50K+ per deal. Alternatively, you can use Waverly to receive a 1st draft of the key insights in minutes. Our partners have been working with us to 1) accelerate diligence and 2) find insights that competitors may have missed. We’re starting with this particular workflow, but we’ll add a lot more capabilities soon. [email protected]
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This is truly one of the most exciting times to see businesses evolve and adopt AI strategies to enable their teams and enhance customer experience. Glean's AI platform is uniquely positioned to enable companies, across the globe, to access and take action on data like never before, across every department and position. Search + Agentic Reasoning can and will change the landscape of every business. The early adopters will have a strategic advantage, in the same way that Glean has now.
The rise of agentic reasoning promises to transform how we work by creating AI systems that can autonomously pursue goals and complete complex tasks. Glean is at the forefront of this shift, pioneering a new agentic reasoning architecture that expands AI’s potential to get work done: resolve support tickets, help engineers debug code, and adapt tone of voice for corporate communications. With our new architecture, agents break down complex work into multi-step plans. Steps are executed by AI agents that are trained on their tasks and equipped with the tools to achieve their goals. Early research shows a 24% increase in relevance with our new agentic reasoning architecture. Here’s a preview of the new architecture: 🔷 Search: Evaluate the query and, using heuristics, determine whether it can be answered using search or agentic reasoning. 🔷 Reflect: Reflect on the initial search results, gauge confidence in the result, and decide whether to return a result or keep going down the agentic reasoning path. Search → fast and accurate answers Agentic reasoning → complex multi-step queries 🔷 Plan: Formulate the strategy, deeply understanding the goal and breaking down the steps to achieve it. Figure out the specialized sub-agents and tools to achieve each step of the work. 🔷 Execute: Sub-agents reason about the tools to use-search, data analysis, email, calendar, employee search, expert search, etc.- and how to stitch them together to achieve individual goals. 🔷 Respond: Respond in natural language via chat or by taking an action like creating a Jira ticket. Reimagining Work AI is an ongoing journey that builds on our foundational technologies. We began with search and advanced to RAG; now we’re progressing from RAG to agentic reasoning. We remain committed to pushing the boundaries of what AI can achieve in the workplace. This is the AI journey we envision for all our customers, where continuous innovation and practical application go hand in hand to transform the future of work. https://bit.ly/3ZdIWvg
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How do you start to explore building an AI agentive solution for your business? 👌 Here is snippet on 3 key factors to consider with a project or process in mind. 🎥Full video: https://lnkd.in/ee8-tzwz 🚀 Excited to share how to go about an AI discovery process ! Here’s a streamlined breakdown: 𝐓𝐰𝐨 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐂𝐚𝐥𝐥 𝐏𝐡𝐚𝐬𝐞𝐬: ➡️Exploration Phase: Shorter calls to spark ideas on AI implementation, covering repetitive administrative, customer-facing, document generation processes, and more ➡️Project-Specific Phase: In-depth calls for clients with a process or project in mind, focusing on understanding actions, cognitive reasoning, and system integrations. 𝐊𝐞𝐲 𝐂𝐚𝐥𝐥 𝐄𝐥𝐞𝐦𝐞𝐧𝐭𝐬: ➡️Process Understanding: Detail each action, integration, and personalization step. ➡️Data Warehousing & Generative Actions: Identify points where AI generates documents or personalized scripts. ➡️Logical Processing: Determine decision-making logic at each step. 𝐏𝐨𝐬𝐭-𝐂𝐚𝐥𝐥 𝐀𝐜𝐭𝐢𝐨𝐧𝐬: ➡️Document everything in a requirements framework. ➡️Confirm details with the client. ➡️Analyze sample output/conversations for practical insights on the client's desired outcomes. ➡️Scope a robust and financially flexible AI solution proposal. Ensuring effective scoping and risk management! 💡 Reach out to us if you're looking to learn more about how AI can help your process! Oleksandr (Alex) Krasun Accelerate AI #AI #Outboundqualification #Automation #DiscoveryCall #AIagents
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#Optimize24 session 1 details. 👇 “#GenerativeAI is not a replacement for human creativity, but rather a tool that can augment and enhance it” – Sam Altman, CEO, OpenAI. Join us for an engaging discussion on #AI and how it fits into #governmentcontracting. We’ll outline where AI applies in the Capture and Proposal development process and show how products (including VisibleThread) will evolve to automate many of these tasks using #GenAI. We’ll cover the right and wrong use cases for GenAI, security, hallucinations, and what you need to know to use it well. This will be a practical, no-nonsense session, with lots of useful takeaways. #proposalmanagement #capturemanagement #artificialintelligence #govcon #governmentcontracting
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This article discusses the increasing significance of AI in various aspects of business operations and process orchestration. It highlights how AI can enhance efficiency, accuracy, and effectiveness in handling tasks and optimizing workflows.
How Artificial Intelligence can Enhance Your Business Process
https://camunda.com
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AI: Task Automation You may have heard about how AI can help automate tasks but what does that mean in your business on a day to day basis? And how do you cut through the sales blurb and get enough information to know what can and can’t be done? Well that’s where pm² comes in. We’ll identify where your business can benefit from the tactical use of AI and where it’s not required (see if any of the big US companies do that for you). We’ve implemented process automation in areas from invoice matching to contract generation, working closely with client business partners to ensure clear KPIs for delivery success. See our latest blog and don’t forget to subscribe when you are on the website. https://lnkd.in/eGH6AvDv
AI Task Automation
pm2.ai
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Are you spending a lot of time on manual admin? Do you want to increase efficiency, and deliver accuracy? 📈 Automating your processes, can reduce cost and deliver real business benefits to an organisation. 🌟 Embridge Consulting have a number of innovative automation solutions designed to take the pain away from your processes, take a look below 👀 - ✔️Invoice Automation ✔️Artificial Intelligence (AI) & Machine Learning ✔️Workflow & Approval Automation Reach out for a chat - [email protected] ✉️ Which area of your process do you struggle with the most? #Automation #DigitalTransformation
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Senior Software Engineer | Cloud -AWS & Azure | Gen AI, RAG, LLMs, Agent | Microservice, RESTful APIs | Dynamics 365 F&O, Field Service & PowerApps
AI agent based system going to be future of software developement and has potential to reduce cost .. it is worth mastering and exploring it
GenAI Evangelist (67k+)| Developer Advocate | Tech Content Creator | 30k Newsletter Subscribers | Empowering AI/ML/Data Startups
Multi #AI agent systems can efficiently handle tasks of varying complexity! Here is a simple workflow of multi AI agent system. ⮕ Task Input: The process begins when a task is introduced into the system. ⮕ Task Complexity Assessment: The system evaluates the complexity of the task. ⮕ Simple Task Route: If the task is deemed simple, it's directed to a Single Agent. The Single Agent processes the task independently. ⮕ Complex Task Route: If the task is complex, it's routed to the Multi-Agent System. The Multi-Agent System consists of specialized agents: A. Agent 1 (Task Division): Breaks down the complex task into manageable subtasks. B. Agent 2 (Processing): Handles the core processing of the divided subtasks. C. Agent 3 (Integration): Combines and integrates the processed subtasks. ⮕ Collaborative Work: For complex tasks, all agents work together. They share information, process in parallel, and coordinate their efforts. ⮕ Output Generation: Both simple and complex task routes converge at this stage. The system compiles the processed information into a coherent output. ⮕ Final Result: The system produces the final result of the task. This could be an answer, a solution, or a completed action, depending on the initial task. This way multi-AI agent systems can efficiently handle tasks of varying complexity, leveraging collaboration for more demanding problems while streamlining simpler ones. Here is my complete video on building multi AI agent systems using LlamaIndex and Crew AI: https://lnkd.in/gpFrJjdS
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