Case Study
An Intelligent Agent Built for
Security Research
Problem Statement
Security analysts face an overwhelming flow of CVE disclosures. Insights are scattered across vendor bulletins, GitHub issues, and mailing lists, making it difficult to triage vulnerabilities quickly and accurately.
Solution
Focaloid has developed a CVE Research Agent using Agentic AI framework that replicates the end-to-end research workflow of a skilled analyst using a team of specialized AI agents that work collaboratively.
How It Works
The CVE Research Agent automates the process of identifying and analyzing vulnerabilities (CVE) using specialized AI agents, ensuring higher efficiency and scalability than human analysts. Here’s a quick overview of the process:
Key Benefits
Speed
Reduces research time from hours to minutes.
Accuracy
Combats misinformation with multi-source validation
Scalability
Handles 1000s of CVEs simultaneously
Integrations
Integrated into analyst workflow - Jira / Slack
Built on Our Agentic AI Framework
The CVE Research Agent is just one example of what’s possible. Our Agentic AI Framework allows us to build intelligent, reusable agents for any domain.
Key Capabilities
Multi-Agent Orchestration
Coordinate autonomous agents using LangGraph + LangChain to execute complex workflows.
Reusable Agent Patterns
Define repeatable agent roles (Planner, Worker, Reviewer, etc.) and compose new ones.
Action-Oriented Design
Agents can take real actions (e.g., create tickets, trigger workflows) and integrate directly with enterprise systems.
Library of Customizable Agents
Extensive library of reusable agents to accelerate custom workflow automation
Pluggable Toolchain
Easily integrate data sources, scrapers, APIs, or internal tools via modular connectors.
Context & Messaging Protocols
Context management with Model Context Protocol (MCP) to maintain structured state across agents
Secure & Deployable
Works in cloud, VPC, or on-prem environments, with support for enterprise authentication.
Inter Agent Communication
Uses Google’s A2A protocol for consistent, modular agent-to-agent messaging.
End-to-End Observability
LangSmith provides full traceability, debugging, and performance metrics across agents.
Co-Creating Agents - Our Approach
We follow a structured approach to co-create intelligent agents that reflect your domain, workflows, and goals:
Key Capabilities
Discovery & Process Mapping
Analyze business context, identify automation opportunities, deconstruct tasks, and assign agent roles.
Agent Design & Decomposition
Define task structures and agent responsibilities.
Tool & Integration Layer Design
Map data access methods and system integrations (APIs, Slack, Jira, databases).
Build, Test & Iterate
Construct agent workflows, validate against real-world scenarios, and refine iteratively.
Deploy & Scale
Launch within secure environments and extend agents across adjacent use cases.
