AI Agents
7 articlesThe Agent Layer That Made ChatGPT 5.5 Medium Fast Work 15x Better
A practical look at how @aimlsuperagent/agent can turn loose AI coding help into a more structured workflow with cleaner completion and less follow-up cleanup.
AiML SuperAgent: The Context Minimizer for Serious AI Coding Work
A single behavior file can make an AI assistant safer for one session. AiML SuperAgent adds the missing operating layer: scoped memory, production checks, deployment discipline, and smaller active context.
Agentic Design Patterns, Part 1: What Makes an AI System Actually Agentic
A model answering a prompt is not an agent. The useful distinction is action-oriented orchestration: goals, tools, state, adaptation, and structured control around the model.
Agentic Design Patterns, Part 2: The Workflow Patterns That Make Agents Reliable
Prompt chains, routing, parallelization, reflection, tool use, planning, and multi-agent coordination are the reusable patterns that turn model capability into system behavior.
Agentic Design Patterns, Part 3: Memory, RAG, MCP, and Human Oversight
Persistent state, retrieval, structured tool protocols, adaptation, and human approval flows are what keep agents useful after the first impressive demo.
Agentic Design Patterns, Part 4: Guardrails, Evaluation, and Production Agent Systems
The difference between a fun agent demo and a production system is usually not smarter prompting. It is exception handling, measurement, resource discipline, and safety architecture.
AutoGPT, AgentGPT & LangChain: Building Your First AI Agent for Profit
AI agents that work while you sleep. Learn how to build, deploy, and monetize autonomous AI agents for lead generation, research, and content creation.