2025's AI Game-Changer: Full-Scale Adoption of Autonomous Agents

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?? 2025 Technology Trend

1?? Full-scale Adoption of AI Agents On October 23, Anthropic unveiled upgraded models—Claude 3.5 Sonnet and Claude 3.5 Haiku—along with a public beta of a new "computer use" feature. This allows Claude to operate a computer like a human, generating significant interest across the tech community.

Claude | Computer use for automating operations

As you can see in the video above, Claude can be directed to use a computer much like a real person through the API, performing tasks such as screen viewing, moving the cursor, clicking buttons, and entering text. This demonstrates Claude's functioning as an AI agent—a type of software that can autonomously interact with its environment to achieve specific goals.?

Market research firm Markets and Markets forecasts that the AI agent market will grow from $5.1 billion in 2024 to $47.1 billion by 2030, with particularly rapid adoption in productivity tools and customer service. Gartner has identified AI agents as the top tech trend for 2025. By 2028, it is predicted that 15% of corporate decision-making will be handled by AI agents, and as adoption costs decrease, conditions will increasingly enable small to medium-sized businesses to implement AI agents as well.

Besides Anthropic, big tech companies are also actively developing AI agents. Microsoft is testing an agent capable of scheduling tasks on Windows PCs, while Amazon is working on a shopping-focused AI agent based on its LLM, "Rufus." Other examples include Salesforce's platform for building and deploying autonomous AI agents, "Agentforce," and LinkedIn's "Hiring Assistant," which helps streamline repetitive tasks like candidate searches and application reviews in recruitment.

Latest Generative AI + Shopping Cases, covering Amazon Rufus, Etsy, Shopify, and more

Enterprise companies also appear to agree on the future direction of AI agents. According to a report published by MIT Technology Review Insights on October 17, 49% of executives believe that AI agents and assistants will lead to increased efficiency and cost savings. A survey by startup accelerator Forum Ventures revealed that 48% of companies have begun adopting AI agents, and 51% of decision-makers are very open to partnering with startups for AI agent solutions.

The Power of Agent RAG Behind AI Agents!?

In September, Oracle introduced a generative AI agent featuring Retrieval-Augmented Generation (RAG) functionality. This makes it easier for companies to seamlessly integrate RAG when using generative AI agents. Oracle’s use cases include enhancing customer satisfaction in call centers through more accurate responses and higher query resolution rates; enabling finance teams to analyze customer purchase histories and identify trends using natural language queries; and retrieving relevant information from large internal data stores to improve work efficiency and productivity.

RAG is a technology that leverages the advantages of retrieval-based and generative models.RAG solutions are being implemented to reduce hallucinations when using generative AI in enterprises.?

As previously mentioned, AI agents operate with goals and autonomy with the ultimate objective of achieving the user-provided task. Currently available RAG solutions often provide one-way, single-response answers, making it challenging to follow up with additional tasks. Now, Agentic RAG technology is becoming essential in corporate AI projects. Agentic RAG is an agent-based approach designed to handle tasks across multiple documents, often collaborating with users when a specific need arises, similarly to a human coworker.?

Allganize’s Agentic RAG dynamically proceeds through a process of planning—sub-agent and tool identification and allocation—and execution to deliver the optimal answer based on internal and external knowledge and data. In this process, it presents the user with candidate responses closest to the ideal answer, helping the user make informed decisions. It also refines the agent’s autonomy by incorporating feedback, allowing it to operate with increasing sophistication.

Features and Advantages of Agentic RAG

• Organized Q&A: Agentic RAG breaks down the Q&A process into small, manageable segments, assigning specific agents to each segment and maintaining smooth coordination to manage the process systematically and achieve the best results.
• Goal-Oriented Approach: Agents are designed to understand and pursue defined goals, allowing for deeper, more meaningful interactions.
• Advanced Planning and Reasoning: Skilled in complex planning and multi-step reasoning, it identifies the most effective strategies to gather, analyze, and synthesize information to solve complex problems.
• Tool Utilization and Adaptability: Agentic RAG agents can leverage external tools and resources, such as search engines, databases, and specialized APIs, to enhance data collection and processing capabilities.
• Context Sensitivity: Agents make informed decisions and execute related tasks by considering the current situation, previous interactions, and user preferences.
• Incremental Learning: Designed to learn and evolve over time, improving its knowledge base and problem-solving abilities whenever it encounters new challenges and information.
• Customization and Flexibility: The Agentic RAG framework offers considerable flexibility, adapting to specific requirements and various domains.
• Enhanced Accuracy and Efficiency: By combining the strengths of large language models (LLM) with agent-based systems, it achieves higher accuracy and efficiency in Q&A compared to traditional models.

?Differences Between Traditional RAG and Agentic RAG

Agentic RAG represents significant progress in the field of question answering and information retrieval. It can efficiently and effectively handle complex queries for internal hyperscale documents, making it particularly useful in environments based on vast amounts of heterogeneous data sources.

Allganize’s Agentic RAG solution contributes to the RAG ecosystem not only by publishing a RAG leaderboard but also through strategies like retriever optimization and answer highlighting . If you’re considering successful strategies for enterprise generative AI projects, explore Allganize’s Agentic RAG. We're here to help! Contact us to explore tailored solutions, insights, or collaboration opportunities in the exciting world of AI agents and beyond.

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