Develop Your Own AI Assistant Powered by Postgres

AI agents are rapidly becoming the cornerstone of SaaS innovation, empowering companies and individuals to achieve more with less effort. They foster a deeper connection between humans and technology by anticipating needs, providing contextual assistance, and integrating smoothly into daily workflows. An agentic AI experience will allow users to seamlessly collaborate with intelligent agents that understand the user’s workflow and can couple personalization with task augmentation.

The past twenty years of SaaS required human inputs to deliver operational reports to managers, but the next decade will shift towards applications that listen to and work for the end user. Like the bot craze of the 2010s, driven by improvements in natural language understanding models, AI agents powered by large language models (LLMs) present a unique opportunity to redefine the software landscape's user experience.

What is an AI Agent?

At a high level, AI agents function like employees with specific skill sets. They are particularly valuable because you can create multiple agents, similar to a team, to tackle complex tasks by breaking them down into smaller components. Each agent handles a component independently, in the correct sequence, and can leverage other agents’ domain expertise to fully complete a task. The scale, complexity, and most importantly, utility of AI agents have created new excitement in their application

Empowering AI Agents

If AI agents will dramatically change our ability to perform complex tasks, what are the building blocks that empower AI agents to do great work? From an agent creation perspective, there will be many tools to define an agent’s capabilities. In the case of global tasks like web search, summarization, and writing a document outline, they will likely be very similar. However, the largest gains will come from their ability to work seamlessly with the applications we use daily and be securely fine-tuned on private data. Whether the agent is working as a personal assistant or as a senior-level employee within a specific domain (e.g., Senior PM, Developer, Support Engineer), successful implementations of AI agents will require native platform integration. Agents will move faster, generate more contextual answers, and understand relevance with higher accuracy when the APIs, data, workflows, and actions they take are centralized. This presents a complex problem, as modern compute companies are typically thin services, while legacy enterprise applications contain much of the business-specific data and processes required to guide an agent.

AgentOS: Streamlining Agent Adoption

To bridge modern compute with legacy platforms, DevRev developed AgentOS to connect humans with agents on one system. Agents are armed with the data and tools they need to perform a wide range of tasks efficiently, and humans are provided with a modern application interface to both define the skills of their agents effortlessly and collaborate with them in realtime. With over 30 integrations, AgentOS indexes disparate data into a knowledge graph that connects the work you do to its relationship with your product and customers. This is coupled with semantic search to support information lookups, a serverless workflow engine to drive real-time automation, and an in-browser analytics engine that empowers AI agents to query and join data for further analysis.

Your agents are only as good as the context you provide them. While the broader industry is offering “seamless integrations” with CRM platforms by pulling in data at runtime, we have taken a different approach by continuously syncing events from all your systems and indexing it during the initial setup. We also maintain a two-way sync to ensure that the indexed data is always up-to-date between legacy systems and your new modern human + AI application.