AI Agents
7 articles tagged "AI Agents"
Explore our collection of articles about ai agents. These posts cover practical insights for enterprise AI governance, compliance, and infrastructure teams.
n8n for AI: What It Is and Why It Suddenly Matters
n8n started as an open-source Zapier alternative. Then 2024 happened, and it became one of the most-downloaded AI orchestration tools on the planet. Here is what changed and where it fits in an enterprise stack.
Agent Workflows in Enterprise Software Development
Single-agent prompting got you a productive autocomplete. Multi-agent workflows turn coding agents into a coordinated team — and that's a different engineering problem.
Integrating AI Agents into Your Existing DevOps Pipeline
You don't need to replace GitHub Actions, Jenkins, or your Jira workflow to adopt coding agents. Here's where agents plug in — and where the governance layer has to live.
The Agent-Team Model: PM, Architect, Developer, QA, Security as Specialised Roles
Software has roles. Agent teams should too. Concrete role definitions, typed handoffs, and why specialisation outperforms generalism for enterprise AI software development.
AI-Native SDLC: Automating Beyond CI/CD
CI/CD automated the last mile of software delivery. AI-native SDLC automates the first mile — design, implementation, and review.
How Vibecoding Agents Leverage MCP Tools
Vibecoding agents don't just write code — they use MCP tools to read files, run tests, query APIs, and push commits. Here's how agents chain MCP tool calls to accomplish real development tasks.
What is MCP? How LLMs Use the Model Context Protocol
A technical deep dive into the Model Context Protocol — the open standard that lets LLMs discover and invoke tools. Learn the architecture, JSON-RPC transport, and the exact tool-call loop that powers AI agents.
Weekly governance insights for engineering leaders
Read the patterns behind governed AI delivery, then turn them into tracked workflows with Axiom Studio.
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