line·Apr 15, 2026·4 min readCurated summary

ODW #2: Developing Single/Multi-Agents with ADK and Integrating with Internal Systems

AI adoption can improve productivity, but relying on individual developers to create and refine local AI agents leads to knowledge silos, duplicated effort, and uneven results. LY Corporation’s Orchestration Development Workshop addresses this by teaching engineers to build shared single- and multi-agent systems with Google’s Agent Development Kit (ADK). The workshop combines theory with hands-on integration of agents and internal tools such as Jira and Confluence through MCP. ## Organizational Need for AI - Potential applications include pull request reviews, customer support, and internal document search. - Information is difficult to find because company knowledge is distributed across systems such as Jira and Confluence. - LY Corporation aims to double work productivity within three years through AI and continuous innovation. - As tools such as Cline and Claude Code spread, usage remains concentrated among individuals. - This creates: - Productivity gaps between employees - AI knowledge silos - Repeated prompt-development work across teams - Limited awareness of multi-agent approaches - Abandonment of AI when single agents cannot handle complex tasks ## Why a Hands-On Workshop The organizers concluded that organization-wide adoption required practical understanding of three areas: - The strengths and limitations of single-agent and multi-agent systems - A team-based model for building and sharing centralized agents - Integration between AI agents and internal systems through the Model Context Protocol (MCP) Rather than teaching only concepts, the workshop required participants to build working agents with ADK. ## Single-Agent and Multi-Agent Systems - **Single agents** - Use one LLM and are relatively inexpensive and simple to develop. - Work well for straightforward tasks. - Struggle with complex problems requiring multiple specialties. - **Multi-agent systems** - Divide work among multiple specialized LLM-based agents. - Can handle more complex workflows and optimize tasks more effectively. - Require more development effort and token usage. - Must be designed carefully to avoid usage limits and excessive costs. ## Introducing Google ADK - ADK is open-source software for defining agent behavior and building multi-agent systems. - It supports Python, Java, and Go. - Python functions can be exposed as tools that agents invoke. - Teams can build and host shared agents, reducing the need for every employee to independently optimize prompts. ## Building a Single Agent Participants practiced: - Running an ADK web UI and interacting with an agent in a browser - Modifying instructions to change agent behavior - Connecting a prepared Python function as an executable tool The exercises demonstrated that prompts can flexibly control responses and that ordinary Python code can be integrated into an agent with relatively little effort. ## Connecting Agents to Internal Systems with MCP - MCP is an open standard for connecting LLMs to external systems. - It enables agents to actively search sources such as previous inquiries, documentation, Jira, and Confluence. - Participants learned that merely exposing tools is insufficient; the agent also needs clear instructions to use them effectively. - Giving one agent too many tools can enlarge its context, slow responses, and reduce accuracy. - Splitting responsibilities across multiple agents can help isolate context and mitigate these problems. ## Building a Sequential Project Tracker The main exercise created a project-tracking system that analyzes Jira projects and produces translated progress reports. - Four agents execute sequentially: 1. Analyze in-progress tasks 2. Analyze todo or unstarted tasks 3. Generate a consolidated Markdown report 4. Translate the report into the configured language - The first two agents use Jira through MCP. - The report generator synthesizes the preceding analyses. - The translator preserves the report’s formatting and structure. - ADK’s `SequentialAgent` coordinates the workflow and passes results between specialized agents. ## Practical Recommendation Organizations seeking broader AI adoption should move beyond individual experimentation. Shared agents built with ADK, connected to internal systems through MCP, can consolidate expertise, reduce duplicated prompt work, and make multi-agent workflows accessible to entire teams.

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Multi AgentAIKubernetesModel Context Protocol+4

toss·Dec 23, 2025Original article

Automating Service Vulnerability Analysis (opens in new tab)

Toss has developed a high-precision automated vulnerability analysis system by integrating Large Language Models (LLMs) with traditional security testing tools. By evolving their architecture from a simple prompt-based approach to a multi-agent system utilizing open-source models and static analysis, the team achieved over 95% accuracy in threat detection. This project demonstrates that moving beyond a technical proof-of-concept requires solving real-world constraints such as context window limits, output consistency, and long-term financial sustainability. ### Navigating Large Codebases with MCP * Initial attempts to use RAG (Retrieval Augmented Generation) and repository compression tools failed because the LLM could not maintain complex code relationships within token limits. * The team implemented a "SourceCode Browse MCP" (Model Context Protocol) which allows the LLM agent to dynamically query the codebase. * By indexing the code, the agent can perform specific tool calls to find function definitions or variable usages only when necessary, effectively bypassing context window restrictions. ### Ensuring Consistency via SAST Integration * Testing revealed that standalone LLMs produced inconsistent results, often missing known vulnerabilities or generating hallucinations across different runs. * To solve this, the team integrated Semgrep, a Static Application Security Testing (SAST) tool, to identify all potential "Source-to-Sink" paths. * Semgrep was chosen over CodeQL due to its lighter resource footprint and faster execution, acting as a structured roadmap that ensures the LLM analyzes every suspicious input path without omission. ### Optimizing Costs with Multi-Agent Architectures * Analyzing every possible code path identified by SAST tools was prohibitively expensive due to high token consumption. * The workflow was divided among three specialized agents: a Discovery Agent to filter out irrelevant paths, an Analysis Agent to perform deep logic checks, and a Verification Agent to confirm findings. * This "sieve" strategy ensured that the most resource-intensive analysis was only performed on high-probability vulnerabilities, significantly reducing operational costs. ### Transitioning to Open Models for Sustainability * Scaling the system to hundreds of services and daily PRs made proprietary cloud models financially unviable. * After benchmarking models like Llama 3.1 and GPT-OSS, the team selected **Qwen3:30B** for its 100% coverage rate and high true-positive accuracy in vulnerability detection. * To bridge the performance gap between open-source and proprietary models, the team utilized advanced prompt engineering, one-shot learning, and enforced structured JSON outputs to improve reliability. To build a production-ready AI security tool, teams should focus on the synergy between specialized open-source models and traditional static analysis tools. This hybrid approach provides a cost-effective and sustainable way to achieve enterprise-grade accuracy while maintaining full control over the analysis infrastructure.

Multi AgentAILarge Language ModelsModel Context Protocol+4