▸ Tag · #agentic-ai
Agentic AI.
Agents that do real work: tool use, multi-step planning, the MCP/A2A/ACP protocol landscape, and keeping autonomy bounded enough to trust.
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ArchitectureMCP first is the new mobile first
Mobile first made every screen work on a phone. MCP first makes every product capability work for an AI agent — tools, skills, and permissions before the human UI.
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LaravelMCP auth and audit logging in a Laravel SaaS
An MCP server on Laravel is only as safe as its tenancy boundary. Scoped tool auth, request-bound actor context, and an audit log you can actually read at 3am.
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AITrust is not a QA strategy: test AI code too
AI code is 95% syntactically clean and fails security tests 45% of the time. Half of agentic PRs ship no tests. The verification pipeline I run instead.
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ArchitectureMCP vs A2A vs ACP: the agent protocol stack
MCP connects agents to tools, A2A connects agents to each other, and ACP has folded into A2A. How the two-layer agent protocol stack fits together in 2026.
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LaravelMCP tool errors agents can actually recover from
Throwing exceptions hides failures from the model. MCP wants isError tool results with actionable text — mapped from Laravel domain failures, audited like auth denials.
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LaravelIdempotency keys for MCP mutations in Laravel
Refunds, seat resets, and password emails need more than audit logging. Client-supplied idempotency keys, server-side short-circuits, and race-safe storage under Octane.
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AIStop reading every line of AI-generated code
Line-by-line review breaks on agent-sized diffs. How I moved to specs, property tests, and a multi-agent review pipeline — and the 3% I still read.
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AIVibe coding vs agentic engineering: which ships?
Vibe coding is for prototypes, agentic engineering is for production. The daily split I use to ship AI-written code without hidden technical debt.
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AIAI vs machine learning: an engineering deep dive
AI is the umbrella, machine learning is the engine. See how the distinction shapes your RAG pipelines, agentic systems, and Laravel or Shopify architecture.
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AIRAG architectures: traditional, agentic, corrective
Compare traditional, agentic, and corrective RAG architectures, with the latency, cost, and accuracy trade-offs that decide which fits your AI app.
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AIML engineer vs AI engineer
ML engineer vs AI engineer: who trains the model, who orchestrates the system, their diverging toolsets, and which role your AI roadmap actually needs.
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AILLM vs AI agent: from prompts to action
The architectural shift from LLMs to autonomous AI agents. How memory, tool-use, and planning turn a stateless model into a system that takes action.
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ArchitectureAPI vs MCP: connecting AI systems
The core differences between traditional APIs and the Model Context Protocol (MCP) — and when to use each to build scalable, agentic AI systems.
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ShopifyShopify UCP quick-start: make your store agent-ready
AI agents are the new buyers, and they can't see your store without a UCP manifest. A quick-start to making your Shopify store agent-ready.
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AIClaude MCP: connecting my dev tools to LLMs
The Model Context Protocol is a USB-C port for LLMs: one MCP server, any host, no MxN integration tax. The architecture, the servers I run, and why it is safe.
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ShopifyShopify Storefront Web Components: headless light
Headless used to mean six engineers and a Hydrogen rebuild. Shopify Storefront Web Components drop products, collections, and cart into any page with a script.
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AIMCP tool-use: building context-aware agents
Build context-aware agents with MCP. How tools, resources, and prompts let one server talk to any client — and kill brittle one-off integrations for good.
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ArchitectureVibe coding: the shift to agentic workflows
MCP, agentic loops, and intent-based engineering. How vibe coding becomes a real architecture pattern, plus the Laravel and MCP stack I run today.
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