AI Agents Concepts
Here we collected some answers to questions related to AI agents.
What are AI agents?
AI agents offer a fundamentally different approach to software, capable of independently analyzing situations and taking action to achieve objectives. This agency is derived from the advanced reasoning capabilities of large language models, representing a substantial leap forward from the static nature of traditional software. This technology enables a more intelligent and adaptive response to the complex demands of life sciences informatics.
How do AI agents work?
AI agents function by perceiving information from their environment through methods like natural language processing (NLP) and data connectors (often using MCP). Next, they reason about this information using large language models (LLM) and planning algorithms to determine the best course of action. When needed, explicit instructions can be provided for desired outcomes. Based on the reasoning, they act autonomously to complete business relevant tasks. The tasks might include generating text, manipulating data, or interacting with other systems.
What is MCP?
MCP (Model Context Protocol) is an open standard designed to connect AI assistants with various data systems like content repositories and business tools. It aims to improve the relevance and quality of AI responses by providing a universal protocol for data access, replacing the need for custom integrations for each data source. This allows developers to build secure, two-way connections between the data and AI tools, ultimately enabling AI systems to maintain context across different data sources and tools for a more seamless and scalable architecture.
What is A2A?
A2A (Agent2Agent) is an open protocol designed to enable AI agents built by different vendors or frameworks to communicate, securely exchange information, and coordinate actions across various enterprise platforms and applications. The goal of A2A is to foster interoperability between AI agents. By providing a standard way for agents to connect, discover capabilities, manage tasks, and negotiate user experience, A2A aims to unlock a future where diverse AI agents can seamlessly work together to automate workflows and drive innovation.
A2A standardizes capability discovery, task management, collaboration (and context passing), and user experience negotiation.
What is RAG?
Retrieval-Augmented Generation (RAG) is a process that enhances the capabilities of large language models (LLMS) by grounding their responses in external knowledge. Instead of solely relying on the information they were trained on, RAG models first retrieve relevant documents or data from a knowledge base based on the user's query. This retrieved information is then incorporated into the prompt, allowing the model to generate more accurate, contextually rich, and up-to-date answers while mitigating issues like hallucination.
What is the difference between MCP and A2A?
MCP helps an AI agent get the information and tools it needs from the outside world, while A2A helps AI agents talk to and work with each other.
A2A is designed to complement MCP: an agent might use MCP to access a tool or data, and then use A2A to communicate the results or coordinate further actions with another agent.
MCP is a standardized way to make data available to an agent, while A2A is a way for agents to communicate with each other.
Are AI agents really so easy?
Yes, and no. Agentic AI is a way to meet underlying business needs and they need to be defined in order go build a useful agent. It's easy to put an agent together. Doing so in a way that would make it a useful long term investment requires careful consideration and planning of the larger ecosystem: data quality, integrations, overall AI governance.
What are the pitfalls?
AI agents, while promising, carry several dangers. Their autonomous nature can lead to unintended consequences and potential cost overruns, while reliability remains a key consideration, especially given the challenges of ensuring consistent performance over time. The absence of real-time human oversight introduces risks with potentially significant consequences, and although safeguards can mitigate model hallucinations and unexpected behavior, they cannot be entirely eliminated. Data security considerations have to be carefully evaluated.
Kupsilla: Expertise Engineered
for Agentic AI
Kupsilla's deep-rooted experience in professional software development provides a solid foundation for delivering quality results when it comes to agentic AI.
Our proven ability to build high-quality custom software solutions ensures that agentic systems developed by us are robust and reliable. We bring a professional and structured approach to the complexities of AI agent development, guaranteeing solutions that are not only innovative but also properly architected, and aligned with your specific business needs.
The following are important areas to assess when deploying agentic AI solutions:
- Articulated business need that can be solved with agentic AI;
- Well understood data landscape with clearly defined access scopes and systems of record;
- Cloud-based or on premise model deployment options for cost and security;
- Reliability expectations and safeguards;
- Integrations with the existing systems, workflows, and agents;
- Monitoring and governance, with clear feedback ways to ensure the agents stays on task;
- User training, adoption, and change management.