The concept of agents or multi-agents is becoming increasingly popular, but what does it mean exactly?
An AI agent is an autonomous program capable of performing specific tasks, either independently or in coordination with other agents, to achieve a given objective. Although an AI agent can be developed using almost any technology, this article focuses on the use of the LangChain framework to illustrate its application.
LangChain stands out for its ability to create AI agents that use an advanced language model as a "thinking engine." This technology allows the easy integration of various tools and systems, the ability to follow complex reasoning based on previous steps, and the capability to respond to user queries intelligently and contextually. With LangChain, AI agents become more flexible, adaptable, and capable of automating complex processes with outstanding precision.
Application Example: Responding to an RFP
Responding to a Request for Proposal (RFP) is a complex task that requires compiling and analyzing large amounts of information from different sources. Here’s how a LangChain AI agent can transform this process:
1. Analysis of the RFP Document
When a RFP is received, the first step is to analyze the document. The LangChain agent starts by ingesting this document, using its language engine to extract key requirements. It identifies mandatory criteria, desirable elements, and specific constraints. Based on these information, it creates an action plan that includes searching for relevant data within the company’s internal systems.
2. Consultation of the CRM
Once the RFP document is analyzed, the agent can connect to the CRM to review past interactions with the client or similar clients. These information allows it to contextualize the proposal by adjusting the commercial pitch and anticipating the client’s specific needs. For example, if the CRM indicates that the client has previously expressed interest in sustainable solutions, the agent can highlight the eco-friendly aspects of the offer.
3. Analysis of Previous Responses
The agent then consults past RFP responses, which are archived in internal databases. It can identify strategies that were successful or unsuccessful in order to adapt its response accordingly. This ability to learn from experience is crucial for continuously improving the quality of proposals.
Consultation of the ERP for Pricing
Finally, to prepare the financial offer, the LangChain agent interacts with the company's ERP to obtain the most up-to-date pricing information. It calculates costs based on the RFP specifications while considering profit margins and pricing policies. The agent can also simulate different pricing scenarios to propose the most competitive offer possible.
5. Generation and Submission of the Proposal
After compiling all these information, the LangChain agent generates a complete proposal that includes not only the technical and financial aspects but also a customized argument aligned with the client’s needs. The proposal can then be validated by a human before being officially submitted.
Conclusion
The use of LangChain AI agents in responding to RFPs perfectly illustrates the potential of this technology to automate and optimize complex processes.
To go further, download our free guide"24 points pour cadrer un projet IA Gen" (only available in French).
