AI Agents

AI 101: Agentic Vector Databases – What Is That?

Search, memory, additional knowledge engine: how the role of databases changes once agents become the primary actors

AI 101:Hermes vs OpenClaw: Local AI Agents Compared

Hermes Agent vs OpenClaw: self-improving loop, layered memory, procedural skills & cron scheduling. Full architectural breakdown by Turing Post.

AI 101: OpenClaw Explained + lightweight alternatives

Everything you need to know about OpenClaw: the tech stack, lightweight alternatives, and why it changed the agentic discourse

State of AI Coding: Context, Trust, and Subagents

Context engineering, agent swarms, verification loops: what the AI Engineer Summit revealed about how agentic coding actually works — and where the IDE is going.

The Agentic Stack, So Far?

A 20-part reference guide for a field in motion

🦸🏻#20: What Coding Agent Wins?

A hands-on comparison of 15 AI coding tools across IDEs, CLIs, full-stack agents, and hybrid platforms

🦸🏻Reward Engineering Explained: Functions, Models & Hacking

Reward engineering is the discipline behind AI agent behavior. Learn reward functions, dense vs sparse rewards, reward models, value functions & hacking

🦸🏻#18: Why Single AI Isn't Enough? How Multi-Agent Systems Redefine AI's Potential

everything you need to know about MAS and the recent research developments in the area

🦸🏻#17: What is A2A and why is it – still! – underappreciated?

everything you need to know about Google’s Agent2Agent protocol (and if Google builds the world’s first agent directory, A2A will be the language it speaks)

🦸🏻#16: Co-Agency as The Ultimate Extension of Human

how AI as a medium shapes our perception, behavior, and culture

🦸🏻#15: Humans as Tools? The Surprising Evolution of HITL in Agentic Workflows

how human-AI co-agency is shaping the future of intelligent systems

🦸🏻#14: What Is MCP, and Why Is Everyone – Suddenly!– Talking About It?

everything you need to know about Model Context Protocol

🦸🏻#13: Action! How AI Agents Execute Tasks with UI and API Tools

we explore UI-driven versus API-driven interactions, demystify function calling in LLMs, and compare leading open-source frameworks powering autonomous AI actions

🦸🏻#12: How Do Agents Learn from Their Own Mistakes? The Role of Reflection in AI

we explore how reflection enables AI to self-correct without always needing external feedback, making systems more reliable and autonomous

🦸🏻#11: How Do Agents Plan and Reason?

we explore recent breakthroughs in reasoning (DeepSeek!) as well as main planning techniques that enable precision and adaptability

🦸🏻#10: Does Present-Day GenAI Actually Reason?

we discuss what 'reasoning' actually means and the modes of thinking that we should work on to get closer to AGI

🦸🏻#9: Does AI Remember? The Role of Memory in Agentic Workflows

we explore SOAR’s legacy, memory types, generative workflows, memory mode in LLM, and AI influence on memory itself

🦸🏻#8: Rewriting the Rules of Knowledge: How Modern Agents Learn to Adapt

Exploring the shift from static rules to dynamic reasoning in modern AI agents

🦸🏻#7: From Agentic AI to Physical AI

How Jensen Huang worked backwards to start creating the future for the world + trends in AI and Robotics to keep an eye on

🦸🏻#6: The Role of Profiling in Agentic Workflows

Exploring How Profiling Shapes Character, Awareness and Decision-Making

🦸🏻#5: Building Blocks of Agentic Systems

What powers an AI agent?

🦸🏻#4: From Butler to J.A.R.V.I.S to Butler Again?

History of Jarvis and and a deep dive into recent π0 model from Physical Intelligence

🦸🏻#3: Illustrating Agentic Vocabulary: Real and Potential Examples

plus a deep dive into agentic workflows

🦸🏻#2: Your Go-To Vocabulary to Navigate the World of AI Agents and Workflows

🦸🏻#1: Open-endedness and AI Agents – A Path from Generative to Creative AI?

We start the new series on AI Agents and Agentic Workflows with an introduction and discussion of open-endedness as an evolutionary approach to creation