Observed Signal · May 11, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Agentic Workflows in Java and Quarkus
A technical walkthrough demonstrating how to build and run agentic workflows on the JVM using the Agentican framework and Quarkus. The guide covers prerequisites (Quarkus, Java 25, Maven/Gradle, an LLM API key), adding the Agentican Quarkus runtime dependency, defining agents/skills/workflows via an agentican-catalog.yaml file, configuring LLM providers (default: Anthropic with claude-sonnet-4-5; OpenAI example using gpt-4o-mini), creating typed workflow instances using Java records, and exposing a REST endpoint to start workflows. It also describes built-in integrations: MCP (Model Context Protocol) and Composio (100+ SaaS toolkits like Slack, Notion, GitHub), plus runtime configuration options for multiple named LLMs and examples of looping and branching workflow steps.
Provides a concrete framework and examples for building agentic LLM workflows on the JVM with Quarkus and integrations to SaaS tools, which can enable automation of research and content workflows relevant to marketing and insights teams.
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Key Takeaways & Evidence Grounding
- Agentican Quarkus runtime module dependency: ai.agentican:agentican-quarkus-runtime:0.1.0-alpha.4
- Prerequisites listed: Quarkus, Java 25, Maven or Gradle, and an LLM provider API key
- Workflows, agents and skills are defined in agentican-catalog.yaml loaded from the classpath (or configured path)
- Default LLM provider is Anthropic with model claude-sonnet-4-5; OpenAI is supported with examples using gpt-4o-mini and API-key configuration
- Agentican includes integrations for MCP (Model Context Protocol) and Composio (100+ SaaS toolkits including Slack, Notion, Linear, Salesforce, GitHub, Google Workspace)
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Agentic AI Workflows for Platform Engineering
This is Part 1 of a technical series explaining how to build agentic AI workflows for platform engineering teams. The author argues that improving developer velocity requires encoding team standards into the workspace (not just better prompts): steering files, skills, and agent definitions that provide persistent, role-specific context for AI agents. The post outlines a layered workspace model (e.g., a .kiro/ directory) that injects non-negotiable rules into every AI interaction, describes specialised agents for tasks like infrastructure authoring and security review, and details tool integrations (ticket trackers, CI/CD, AWS). The assumed stack includes AWS (multi-account), Terraform, GitLab CI, and AWS Secrets Manager. The article provides immediate starter steps (create a steering file and AGENTS.md) and previews later parts covering detailed steering files and GitOps/Kubernetes tooling.
Google Android Build In-App Agentic Workflows
Google's Android Developers Blog has published a technical guide on building in-app agentic workflows for Android apps, using cloud-hosted backends. The post introduces the Agent Development Kit (ADK), along with two protocols: AG-UI for standardizing agent-client communication and A2UI for dynamically describing UI components. Developers can use these to create autonomous multi-agent systems, such as a Booking Assistant in the Jetpacker sample app, that handle complex tasks like booking flights and hotels. The backend agents run autonomously, while the Android client renders interactive UI via Jetpack Compose using the new androidx.a2ui libraries.
Agentic Systems: It's the Loop, Not Just the LLM
A Dev.to post by Hemantkumargiri argues that what makes an AI system agentic is not merely pairing an LLM with tools, but the execution loop that surrounds it. The author outlines the agentic workflow (goal → reason → act → observe → repeat → done) and highlights system-engineering challenges necessary for reliable agents: state management, tool selection, error handling, retries, guardrails, termination conditions, and human intervention. The piece reframes agent development as largely a system-design problem rather than purely prompt engineering.
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