Latest news with #LangChain
Geeky Gadgets
5 days ago
- Business
- Geeky Gadgets
How Deep Agents Are Redefining Complex Problem-Solving in AI
What if you could build an AI system capable of not just completing simple tasks but orchestrating complex, multi-step operations with the finesse of a seasoned strategist? Enter the world of AI deep agents, a new evolution in artificial intelligence that combines adaptability, precision, and long-term planning. Imagine an agent that can dynamically adjust to shifting circumstances, delegate tasks to specialized sub-agents, and manage intricate workflows—all while learning and improving from feedback. This isn't science fiction; it's a fantastic leap in AI technology, powered by the robust Langraph framework. Whether you're an AI enthusiast or a seasoned developer, the potential here is staggering: deep agents promise to redefine how we approach complex problem-solving. In this hands-on breakdown, the LangChain team guide you through the essential components and strategies for implementing AI deep agents effectively. From understanding their modular architecture and virtual file systems to exploring tools like dynamic state management and sub-agent delegation, you'll uncover how these systems operate with remarkable efficiency. But this isn't just about tools—it's about unlocking the ability to design agents that think, adapt, and execute with precision. Along the way, you'll discover how LangChain's framework enables you to customize and scale these agents for your unique needs. Ready to explore the mechanics of this innovative technology? Understanding AI Deep Agents What Are AI Deep Agents? AI deep agents are designed to address complex problems by planning and executing tasks over extended periods. They operate within the Langraph framework, which structures agents as graphs to optimize decision-making processes. At the heart of their functionality lies an iterative loop: the agent selects actions, executes them, and processes feedback to refine its strategy. This continuous cycle of action and adjustment ensures adaptability and efficiency, making deep agents particularly well-suited for tackling multifaceted objectives. These agents are not limited to static operations. Instead, they dynamically adapt to changing circumstances, using their ability to process feedback and adjust their approach. This adaptability is a defining characteristic, allowing them to handle tasks that require both precision and flexibility. Key Components of Deep Agents Understanding the core components of deep agents is essential to appreciate their functionality and potential. Each component plays a critical role in making sure the agent's adaptability and effectiveness. State Management: Deep agents dynamically track context, maintaining detailed records of messages, task progress, and a virtual file system. This ensures continuity and adaptability as tasks evolve, allowing the agent to respond effectively to changing requirements. Deep agents dynamically track context, maintaining detailed records of messages, task progress, and a virtual file system. This ensures continuity and adaptability as tasks evolve, allowing the agent to respond effectively to changing requirements. Planning Tool: A built-in to-do list organizes tasks into categories such as pending, in-progress, or completed. This structured approach enables agents to update and manage tasks efficiently, making sure no step is overlooked. A built-in to-do list organizes tasks into categories such as pending, in-progress, or completed. This structured approach enables agents to update and manage tasks efficiently, making sure no step is overlooked. Virtual File System: Simulated as a dictionary, the virtual file system supports scalability and parallel processing. It includes tools for reading, writing, editing, and listing files, allowing seamless task execution and efficient data management. These components work in harmony to provide a robust foundation for deep agents, making sure they can handle even the most demanding tasks with precision and efficiency. Implementing AI Deep Agents a Technical Walkthrough Watch this video on YouTube. Master AI Deep Agents with the help of our in-depth articles and helpful guides. Built-in Tools for Streamlined Operations Deep agents are equipped with a suite of built-in tools that simplify task management and execution. These tools are designed to enhance the agent's functionality and ensure smooth operations. Write To-Dos: This tool updates and monitors the task list, making sure progress is tracked effectively and tasks are completed in a timely manner. This tool updates and monitors the task list, making sure progress is tracked effectively and tasks are completed in a timely manner. File System Tools: A comprehensive set of tools for managing the virtual file system, including: ls: Lists files in the virtual file system, providing an overview of available resources. read file: Reads file content with options for line offsets and limits, allowing precise data access. write file: Writes content to a file, making sure important data is stored securely. edit file: Performs string replacements within files, allowing for efficient content updates. A comprehensive set of tools for managing the virtual file system, including: These tools are integral to the operation of deep agents, providing the functionality needed to manage tasks and data effectively. Sub-Agent Architecture: Enhancing Scalability One of the most innovative features of deep agents is their sub-agent architecture. Sub-agents are specialized entities assigned to specific tasks, each equipped with tailored tools and instructions. Defined by their name, description, prompt, and accessible tools, sub-agents operate under the supervision of the main agent. The main agent delegates tasks to sub-agents and consolidates their results, making sure a cohesive workflow. This modular design enhances scalability, allowing for task specialization and efficient resource allocation. By using sub-agents, deep agents can tackle complex projects that require a high degree of coordination and expertise. Customizing Deep Agents Deep agents offer extensive customization options, allowing you to tailor their functionality to meet specific needs. You can define custom tools, instructions, models, and sub-agents to enhance their capabilities. For instance, the default Claude model is particularly effective for tasks requiring extensive output. However, you can also create custom tools, such as a specialized search function, and integrate them seamlessly with the agent. This flexibility allows you to adapt the agent to a wide range of applications, from managing intricate workflows to developing bespoke solutions. Design Considerations for Implementation When implementing deep agents, several design factors should be carefully considered to ensure optimal performance and functionality. Conflict Resolution: While basic mechanisms handle parallel file updates, more advanced strategies may be required for comprehensive conflict management, particularly in scenarios involving multiple sub-agents. While basic mechanisms handle parallel file updates, more advanced strategies may be required for comprehensive conflict management, particularly in scenarios involving multiple sub-agents. Stateless Sub-Agents: Sub-agents do not retain state, focusing solely on the tasks assigned to them. This simplifies their operation but requires careful task delegation to ensure efficiency. Sub-agents do not retain state, focusing solely on the tasks assigned to them. This simplifies their operation but requires careful task delegation to ensure efficiency. Detailed Prompts: Clear and precise prompts are essential for guiding agent behavior. Well-defined instructions ensure tasks are executed as intended, minimizing errors and maximizing efficiency. By addressing these considerations, you can ensure that your deep agents are well-equipped to handle the challenges they encounter. How to Implement Deep Agents The implementation process involves several key steps, including creating agents, managing states, defining tools, and integrating sub-agents. Modularity is a central feature of deep agents, allowing you to extend and refine their capabilities over time. To implement deep agents effectively, it is essential to adopt a structured approach. Begin by defining the agent's objectives and identifying the tools and sub-agents required to achieve them. Next, configure the agent's state management and planning tools to ensure seamless operation. Finally, test and refine the agent's functionality to address any potential issues and optimize performance. By following these steps, you can use deep agents to tackle complex, multi-step challenges with confidence and precision. Media Credit: LangChain Filed Under: AI, Technology News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Time Business News
01-08-2025
- Business
- Time Business News
Top AI Development Company India for Smart Automation
Explore AI Agent Development for Smart Automation and transform how your business operates in 2025. With companies facing rising demands for speed, accuracy, and intelligence, the need for AI-powered systems is more urgent than ever. Businesses are increasingly turning to India for cutting-edge solutions in AI development, particularly in the field of smart automation. From AI agents to Generative AI applications, Indian companies are leading the way in delivering scalable, cost-efficient automation solutions. As AI continues to evolve, the integration of smart systems is helping organizations shift from reactive operations to proactive, autonomous workflows. Smart automation refers to the integration of artificial intelligence, machine learning, and intelligent process automation to streamline workflows. It enables systems to make decisions, predict outcomes, and act without human intervention. Unlike traditional automation, which follows static rules, smart automation is adaptive and data-driven. For example, AI agents can monitor inventory, trigger supply chain actions, or generate performance reports—all without manual input. These capabilities significantly reduce operational costs and improve efficiency. Companies that embrace AI-powered automation gain a competitive edge by increasing speed, lowering errors, and optimizing resource use. India has become a preferred destination for AI solutions due to its skilled talent pool, strong tech ecosystem, and cost advantage. Developers in India have deep experience with AI tools like TensorFlow, PyTorch, LangChain, and OpenAI APIs. This expertise enables rapid development of Generative AI agents, smart chatbots, and process automation bots. Additionally, Indian AI companies understand both legacy systems and cloud-native platforms. This dual capability allows them to offer end-to-end solutions—whether it's upgrading legacy ERP with IoT and AI, or building a new AI-first application from scratch. Many global enterprises now rely on Indian developers to power their AI transformation journey. Smart automation depends on a mix of AI subfields and development tools. These include: Generative AI for content creation, language modeling, and insight generation for content creation, language modeling, and insight generation AI agents that autonomously manage tasks, alerts, and workflows that autonomously manage tasks, alerts, and workflows Natural Language Processing (NLP) for chatbots and voice interfaces for chatbots and voice interfaces Computer vision for facial recognition, defect detection, and surveillance for facial recognition, defect detection, and surveillance IoT integration for real-time data from machines and environments All these technologies come together to build intelligent, adaptive systems. These systems not only automate but also learn, improve, and scale with time. See How IoT-Enabled ERP with AI Powers Smart Operations with seamless device-to-cloud integrations. Smart automation is not just a technical solution—it benefits stakeholders across the organization. For example: Executives get real-time business dashboards powered by AI insights. get real-time business dashboards powered by AI insights. HR teams use AI tools to screen resumes and automate onboarding. use AI tools to screen resumes and automate onboarding. Sales departments rely on smart CRMs with AI lead scoring and follow-up recommendations. rely on smart CRMs with AI lead scoring and follow-up recommendations. Customer support is transformed by AI chatbots and voice assistants. All of this results in faster decisions, reduced human error, and improved user experience. With no-code and low-code interfaces, even non-technical users can manage AI workflows and dashboards effectively. Businesses are already implementing AI automation across industries: In manufacturing , AI agents handle predictive maintenance and quality inspection. , AI agents handle predictive maintenance and quality inspection. In finance , automation bots manage audits, reconciliations, and fraud detection. , automation bots manage audits, reconciliations, and fraud detection. Retail companies use AI to optimize pricing, manage inventory, and automate promotions. use AI to optimize pricing, manage inventory, and automate promotions. Healthcare providers deploy AI for patient triage, smart diagnostics, and robotic process automation. These use cases prove that AI is no longer experimental—it's a reliable engine of productivity and intelligence. 'Working with Amar InfoTech was a game-changer. They built a custom AI agent system that automated over 60% of our support tickets. Our operations became leaner and smarter within months.' – Neeraj Shah, COO, FinSync Global Generative AI is transforming how businesses create, interact, and operate. It powers AI agents that can write emails, generate reports, or even design marketing content. When embedded into ERP systems, it can convert raw data into natural-language summaries, reducing manual reporting. Developers in India are rapidly adopting large language models (LLMs) and building tools that allow AI to reason, generate, and act—closing the gap between humans and machines. As these tools become more integrated, companies can scale faster without increasing headcount or infrastructure. Discover Generative AI Solutions from India's Leading AI Development Company and reshape your workflows for the AI era. Amar InfoTech stands out as a top AI development company in India due to its deep focus on smart automation, custom AI agent development, and generative AI integration. With proven success across industries, Amar InfoTech helps businesses adopt AI with real ROI. Clients benefit from: Agile AI teams with cross-industry experienc Modular AI-powered systems Transparent pricing and milestone-based delivery Support for cloud, hybrid, and on-premise deployments Their ability to mix innovation with reliability makes them a trusted partner for mid-sized to large enterprises globally. TIME BUSINESS NEWS
Geeky Gadgets
31-07-2025
- Business
- Geeky Gadgets
Introducing Align Evals : The Ultimate Tool for AI Precision and Efficiency
What if evaluating the performance of large language models (LLMs) could be as precise and seamless as setting a GPS to your destination? With the rapid rise of LLM applications in everything from creative writing to technical problem-solving, making sure these models meet user expectations has become a critical challenge. Yet, traditional evaluation methods often feel like navigating uncharted terrain—time-consuming, inconsistent, and prone to misalignment between machine outputs and human judgment. Enter Align Evals, a new feature introduced by Langsmith, designed to bring clarity and structure to the evaluation process. By aligning machine-generated assessments with human-labeled benchmarks, Align Evals promises not only greater accuracy but also a streamlined workflow that enables users to refine their applications with confidence. LangChain explain how Align Evals transforms the way developers and researchers evaluate LLM-generated outputs. From its ability to detect and resolve misalignments to its iterative prompt refinement tools, Align Evals offers a comprehensive framework for achieving consistency and reliability in LLM applications. Whether you're perfecting recipe titles or tackling complex technical content, Align Evals adapts to your unique scoring criteria, making sure your outputs align with human expectations. By the end, you'll discover how this tool not only saves time but also enhances the quality of your applications, bridging the gap between innovation and precision. The question is: how will you harness its potential? Streamlining LLM Evaluations The Purpose and Role of Align Evals Align Evals is built to make the evaluation of LLM outputs both accessible and precise. Its primary objective is to determine whether machine-generated content meets specific scoring criteria by comparing it to human-labeled benchmarks. This alignment process minimizes discrepancies, ensures evaluations reflect human judgment, and ultimately enhances the overall quality of LLM outputs. By bridging the gap between human expectations and machine-generated results, Align Evals enables users to create more reliable and consistent applications. How the Align Evals Workflow Operates The workflow of Align Evals is designed to simplify the evaluation process while maintaining flexibility and adaptability. It follows a structured, step-by-step approach that includes: Gathering representative sample runs: Collect outputs from your LLM application that represent the range of its performance. Collect outputs from your LLM application that represent the range of its performance. Labeling samples with human expertise: Use human input to create a reliable benchmark for evaluation. Use human input to create a reliable benchmark for evaluation. Iterative refinement of prompts: Continuously adjust and refine prompts to ensure the LLM's evaluations align with human-labeled data. This iterative process ensures that the evaluation remains dynamic, allowing you to adapt as your application evolves. By following this workflow, you can identify and address inconsistencies, making sure that your LLM application meets the desired standards. How Align Evals Improves Large Language Model Performance Watch this video on YouTube. Here are more detailed guides and articles that you may find helpful on LLM evaluation. Handling Evaluations and Scoring Criteria Align Evals enables you to use the LLM itself as a judge to score outputs against predefined criteria. For example, if you are evaluating recipe titles, you might establish a rule to avoid unnecessary adjectives or overly complex phrasing. By iteratively refining prompts and evaluators, Align Evals ensures the scoring process aligns with your specific standards. This approach not only enhances the accuracy of evaluations but also helps identify and resolve misalignments effectively. The tool's ability to adapt to different scoring criteria makes it suitable for a wide range of applications. Whether you are evaluating creative content, technical outputs, or user-facing text, Align Evals provides the flexibility needed to meet your unique requirements. Key Features of Align Evals Align Evals is equipped with a comprehensive set of tools designed to support and streamline the evaluation process. These features include: Evaluator creation and modification: Build, test, and refine evaluators to assess LLM outputs effectively. Build, test, and refine evaluators to assess LLM outputs effectively. Iterative prompt refinement: Continuously improve prompts to align machine evaluations with human-labeled benchmarks. Continuously improve prompts to align machine evaluations with human-labeled benchmarks. Misalignment detection and resolution: Identify discrepancies between machine and human evaluations and address them systematically. Identify discrepancies between machine and human evaluations and address them systematically. Progress tracking tools: Monitor alignment improvements over time to ensure consistent evaluation quality. These features work together to provide a robust framework for evaluating LLM applications. By using these tools, users can achieve greater consistency, accuracy, and efficiency in their evaluation processes. A Practical Example: Evaluating Recipe Titles To illustrate the functionality of Align Evals, consider a scenario where you are tasked with evaluating recipe titles. Your goal might be to ensure that the titles are concise, clear, and free from unnecessary adjectives. Using Align Evals, you can follow these steps: Define the evaluation criteria: Establish clear rules, such as avoiding overly descriptive language or making sure brevity. Establish clear rules, such as avoiding overly descriptive language or making sure brevity. Label sample titles with human input: Create a benchmark by labeling a set of sample titles according to the defined criteria. Create a benchmark by labeling a set of sample titles according to the defined criteria. Refine the LLM's evaluation prompts: Adjust prompts iteratively until the LLM's scoring aligns with your expectations. This process not only saves time but also ensures that the evaluation outcomes are consistent and aligned with your goals. By automating parts of the evaluation while maintaining human oversight, Align Evals strikes a balance between efficiency and accuracy. Inspiration and Availability Align Evals draws inspiration from Eugene Yan's research on 'Align Eval,' which emphasizes the importance of aligning LLM evaluations with human preferences. Now widely available, Align Evals offers a user-friendly interface and a suite of powerful tools to enhance the evaluation process. Its design prioritizes accessibility and precision, making it an invaluable resource for developers and researchers working with LLM applications. By incorporating insights from research and practical use cases, Align Evals provides a reliable and adaptable solution for evaluating machine-generated outputs. Its availability ensures that users across various industries can benefit from its capabilities, improving the quality and reliability of their LLM applications. Enhancing LLM Applications with Align Evals Align Evals represents a significant advancement in the evaluation of LLM-generated outputs. By aligning machine evaluations with human-labeled data, it ensures greater accuracy, reliability, and consistency. Whether you are refining prompts, addressing misalignments, or defining specific scoring criteria, Align Evals offers a structured and efficient solution to meet your needs. With its robust features and intuitive design, this tool enables users to align LLM-generated content with human preferences, streamlining the evaluation process and enhancing the quality of applications. Media Credit: LangChain Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Techday NZ
30-07-2025
- Business
- Techday NZ
Linux Foundation adopts AGNTCY to standardise agentic AI
The Linux Foundation has announced that it is welcoming the AGNTCY project, an open source initiative aimed at standardising foundational infrastructure for open multi-agent artificial intelligence (AI) systems. AGNTCY delivers core components required for discovery, secure messaging, and cross-platform collaboration among AI agents that originate from different companies and frameworks. The project has the backing of industry players including Cisco, Dell Technologies, Google Cloud, Oracle, and Red Hat, all of whom have joined as formative members under the Linux Foundation's open governance. Originally released as open source by Cisco in March 2025 with collaboration from LangChain and Galileo, AGNTCY now includes support from over 75 companies. Its infrastructure forms the basis for the so-called 'Internet of Agents' - an environment where AI agents from diverse origins are able to communicate, collaborate, and be discovered regardless of vendor or execution environment. The increasing adoption of AI agents across industries has led to concerns about fragmentation and the formation of closed silos, constraining agents' ability to communicate across platforms securely and efficiently. AGNTCY's infrastructure aims to address these issues by standardising secure identity, robust messaging, and comprehensive observability. This allows organisations and developers to manage AI agents with improved transparency, performance, and trust. Compatibility is a focus for AGNTCY, which is interoperable with the Agent2Agent (A2A) project, also part of the Linux Foundation, as well as Anthropic's Model Context Protocol (MCP). The project supports agent discovery through AGNTCY directories, enables observable environments using AGNTCY's software development kits (SDKs), and utilises the Secure Low Latency Interactive Messaging (SLIM) protocol for secure message transport. "The AGNTCY project lays groundwork for secure, interoperable collaboration among autonomous agents," said Jim Zemlin, executive director of the Linux Foundation. "We are pleased to welcome the AGNTCY project to the Linux Foundation to ensure its infrastructure remains open, neutral, and community-driven." The AGNTCY project's infrastructure offers several key functions for multi-agent environments. Agent discovery is facilitated using the Open Agent Schema Framework (OASF), allowing agents to identify and understand each other's capabilities. Agent identity is supported via cryptographically verifiable processes to ensure secure activity across organisational boundaries. The agent messaging component supports various communication modes, including human-in-the-loop and quantum-safe options via the SLIM protocol. Observability functionalities provide evaluation and debugging across complex, multi-vendor workflows. "Building the foundational infrastructure for the Internet of Agents requires community ownership, not vendor control," said Vijoy Pandey, general manager and senior vice president of Outshift by Cisco. "The Linux Foundation ensures this critical infrastructure remains neutral and accessible to everyone building multi-agent systems." The project is underpinned by real-world applications, including AI-driven continuous integration and deployment pipelines, multi-agent IT operations, and the automation of telecom networks. This underlines the diversity of use cases benefitting from AGNTCY's open source approach. Various leaders and members have shared their perspective on the announcement: "Interoperability is central to Dell's agentic AI vision. The ability of agents to work together empowers enterprises to reap the full value of AI. Additionally, interworking technologies must accommodate agents wherever they are deployed whether in public clouds, private data centres, the edge or on devices. Dell is working hand-in-hand with industry leaders to establish open standards for agentic interoperability. Being a formative member of the Linux Foundation's AGNTCY project is one such step towards fulfilling the promise of agentic AI." – John Roese, global CTO and chief AI officer, Dell Technologies. "We've been building AGNTCY's evaluation and observability components from day one because reliable Agents cannot scale without purpose-built monitoring. Moving all components of AGNTCY to the Linux Foundation ensures these tools serve the entire ecosystem, not just our customers. As a founding member of AGNTCY, we're eager to see neutral governance accelerate adoption of standards we know enterprises need for production agent deployments." – Yash Sheth, co-founder, Galileo. "Open, community-driven standards are essential for creating a diverse, interoperable agentic AI ecosystem. We're pleased that Cisco is moving AGNTCY to the Linux Foundation, where it will be neutrally governed alongside the Agent2Agent protocol to advance powerful, collaborative agent systems for the industry." – Rao Surapaneni, vice president, business applications platform, Google Cloud. "Enterprise customers need agent infrastructure they can trust for mission-critical workloads. We welcome AGNTCY's move to the Linux Foundation and are proud to be a formative member of this project. A tight control over data security and governance helps discovery, identity, and observability components work reliably across the entire enterprise technology stack, not just specific vendor ecosystems." – Roger Barga, senior vice president, AI & ML, Oracle Cloud Infrastructure. "Our customers and partners, as well as the open source communities we work with, are actively exploring agentic capabilities to bring the inferencing benefits of vLLM and llm-d to their applications. Red Hat welcomes AGNTCY's move to the Linux Foundation and we look forward to working with the community to help bring open, agnostic governance to the agentic AI ecosystem." – Steve Watt, vice president and distinguished engineer, Office of the CTO, Red Hat. Follow us on: Share on:
Time of India
28-07-2025
- Business
- Time of India
From Conversations to Execution: The Rise of AI Agents
Live Events Over the past few years, conversational AI tools such as ChatGPT have become household names thanks to their capacity to create responses with real-time answers and engage in human-like conversation. But today, the discussion is changing, literally! Meet AI agents: smart systems that don't merely react to inputs but act on your behalf. While conversational AI is reactive-waiting to be told what to do to give an answer, AI agents are proactive, self-directed, and goal-oriented. Consider them not chatbots, but virtual colleagues that can design, implement, and finish entire workflows on their own without needing constant essential difference is autonomy. A conversational AI may advise you on the five best tools for automating your email marketing. An AI agent will create the campaign, schedule the emails, track performance, and adjust parameters based on real-time data. This shift from "talking" to "doing" is a turning point for artificial intelligence. AI agents are able to call APIs, invoke multiple tools at once, reason out multi-step operations, and even make decisions on dynamic inputs. They run cross-platform, process end-to-end, and self-correct from paradigm is spreading across sectors. Companies are now testing AI agents for customer service, internal operations, tracking finance, scheduling, and logistics. Having the capability to offload repetitive, rule-based tasks to digital workers opens enormous efficiency, decreases human mistakes, and liberates human teams for more strategic roles. It's not about conserving time—it's about reimagining how work is don't need a PhD in machine learning to adopt AI agents. Start with finding the repetitive, rule-based tasks in your process—such as managing leads, dashboards, or calendars. Then, AutoGPT, LangChain, and Reka make it simpler to create or deploy agents that fit your requirements. Most of these tools have plug-and-play APIs, CRM and calendar integration, and natural language interfaces that reduce the technical hurdle. Begin small experiments with AI agents in-house before rolling them out across teams or maximize adoption, equip your agents with pertinent information, provide them with access to critical tools, and regularly review their performance. Feedback loops are what these systems live on. With time, they not only perform better but start to anticipate your needs—turning them from helpers into effect, AI agents are a major step forward in artificial intelligence—going from reactive tools to active partners. Conversational AI enabled us to speak with machines. AI agents are enabling us to collaborate with them. This change is not about substituting for humans, but it's about augmenting our abilities and unloading the operations clutter that sucks productivity away. The future isn't merely AI that speaks-it's AI that delivers.
