【Next-Generation RAG Has Arrived】Technica Launches Agent-Based RAG Service “AIKNOW 2.0”
東京、2025年6月30日、 5:40 PM
Technica Corporation (Headquarters: Chiyoda-ku, Tokyo; CEO: Keiju Tsurubuchi) has released version 2.0 of its knowledge platform “AIKNOW.” AIKNOW 2.0 adopts an advanced “Agent-Based RAG” architecture that goes beyond the conventional RAG model adopted by many companies, delivering greater flexibility and precision in enterprise operations.
【Limitations of Traditional RAG】
Traditional RAG (Retrieval-Augmented Generation) combines two mechanisms: “retrieval” and “generation.” The retrieval component searches a vast database for relevant information or documents based on the user’s query, while the generation component composes a natural and easy-to-understand response by combining that information with the original query.
This mechanism has greatly contributed to improving business efficiency, quality, and knowledge reuse by integrating companies’ proprietary, internal information with general knowledge acquired by large language models (LLMs).
However, traditional RAG has faced the following challenges:
Shallow retrieval: Lacks deep contextual understanding, multi-step reasoning, and the ability to comprehend complex causal relationships.
Low precision and consistency: Often includes irrelevant or redundant information due to insufficient evaluation of relevance or logical structure.
Poor query intent recognition: Struggles to grasp ambiguous queries or business-specific intentions, leading to suboptimal information delivery.
To address these limitations, AIKNOW introduces hybrid retrieval by combining graph-based search utilizing a knowledge graph and vector-based semantic search. In addition, it implements re-ranking based on the relationship between the query and retrieved content, using advanced technologies to deliver more accurate results.
【Features of the Agent-Based RAG】
To further enhance functionality, AIKNOW 2.0 employs an agent-based RAG architecture. This next-generation approach evolves beyond conventional RAG by incorporating advanced capabilities such as agent-based reasoning, branching logic, and multi-step inference.
1. Multi-Step Reasoning and Actions
Instead of a simple “one-time retrieval → generation” flow, the system can perform multiple searches and reasoning steps as needed. It also supports branching flows, external APIs, and calculation tools depending on the context.
2. Intent Understanding and Flexible Planning
The agent autonomously determines “what is not known” and “how deep to investigate,” guiding the reasoning process. It filters and structures retrieved information step by step to construct a well-formed answer.
3. Integration with Knowledge Graphs and Workflows
It can link with structured data like knowledge graphs to understand and handle logical and causal relationships.
4. Self-Feedback for Continuous Improvement
After generating an answer, the system can perform automatic self-checks or additional searches to evaluate whether the answer is sufficient.
【Future Outlook】
Introducing agent-based RAG into enterprise systems is expected to bring the following benefits:
1. Flexibility in Handling Complex Queries
Through multi-step reasoning and branching logic, the system can flexibly and accurately handle complex questions and exceptional cases that were previously difficult to address.
2. Maximized Efficiency and Knowledge Utilization
The agent automatically filters and restructures necessary information, significantly reducing the burden on staff and improving operational efficiency.
3. Enhanced Consistency, Accuracy, and Quality
With support from knowledge graphs and self-feedback, it delivers logically consistent and highly accurate responses, contributing to better customer support and internal knowledge sharing.
Technica aims to help organizations fully leverage their information assets through AIKNOW 2.0, enabling more efficient operations and sustainable business transformation. The company will continue to unlock the potential of generative AI and deliver practical solutions to strengthen customer competitiveness and build smarter work environments.
【Free Webinar Announcement】
To help you gain a deeper understanding of AIKNOW 2.0, we are pleased to host the following free webinar:
The Arrival of Next-Generation RAG: Agentic RAG Leading the Frontline of Operational Transformation
— Autonomous search strategy, integrated data, and accelerated decision-making —
Date & Time: Thursday, July 24, 2025 | 12:00 – 13:00 JST
Format: Online (Zoom)
Participation Fee: Free (registration required)
Register here
Recommended for:
Organizations with extensive knowledge assets that are underutilized
Those facing challenges in knowledge sharing or reliance on individual expertise
Teams seeking to streamline non-routine tasks and exception handling
Companies not seeing expected results from RAG or chatbots
Teams aiming to move beyond PoC and pursue practical AI implementation and monetization
【About Technica Corporation】
Technica Corporation consists of around 50 AI scientists and engineers and offers end-to-end services from AI consulting to system development. With deep expertise in machine learning, deep learning, and generative AI, Technica has built a strong track record both in Japan and internationally.
The leadership team combines strong academic and technical backgrounds with deep business acumen, guiding clients from strategy development through full-scale implementation. By integrating cutting-edge technology with operational insights, Technica supports clients in achieving long-term growth.
