@bindureddy
LLM-based Autonomous Agents - LLM Apps that perform Human-like Tasks The real promise of Large Language Models is their decision-making, reasoning, and human-level cognition skills. AI agents are LLM apps that can be used to perform a specific task - e.g. respond to customer service queries or summarize and extract key terms in a contract To create and run an AI agent, you need a robust platform that ideally has all of these modules Data connectors: You will need to access your data so the LLMS can analyze it and make decisions. Ideally, you need connectors to Snowflake, BigQuery, Salesforce, Confluence, Sharepoint, and such. Data transformations and processing: Every data scientist knows that the raw data is useless and there is always data pre-processing and data cleaning involved. With text documents, OCR (object character recognition) and document chunking are essential steps before you can feed the data into LLMs Prompt engineering: Unfortunately today's LLMs need a significant amount of prompt engineering to perform a particular operation. For example, you will need to prompt it to "think step by step" or "act like you are an expert data scientist". Code execution: It's impossible to write a complex application without writing and executing code. You will want to write Python functions to parse LLM output, engineer LLM context, or apply some routing logic Chaining and Pipelines: Most of these apps will require a pipeline. As an example - a simple app that "extracts key terms from all contracts and stores them in a database table" involves a process that picks up new contracts on a regular basis, applies OCR, chunks the contracts, sends them to the LLM, parses the output and then updates the database. There are a number of steps involved and they can be automated with a pipeline Simpler agents won't require a pipeline but may require chaining multiple LLM calls and executing code to process their outputs. Machine learning models: This is an optional step but you may want to train ML models on some of your data. For example, you may train a forecasting model and then process the forecasts using an LLM UX interface: Some agents (e.g. customer service agents) may be deployed as chatbots. In this case, you need a ChatGPT-like interface for each of your users. You need to save, thread, and collect feedback from chats. To create and deploy these AI agents, we need connectors, data transformers, code execution machines, pipelines, multiple system prompts, and a chatbot interface. An end-to-end LLMOps platform like Abacus, helps you create powerful Agents in a couple of hours. Read more here - https://t.co/NcUcS2YZba