Mert Palazoğlu
Mert Palazoglu has been an industry analyst at AIMultiple since 2023.
Research interests
His work focuses on the latest trends, companies, and innovations in:- Artificial intelligence (AI)
- Cyber security with a focus on network security
- Customer service software
- Proxies for data collection
Education
He graduated with a BS in Management from Bilkent University in 2021.
Latest Articles from Mert
12 Reasons AI Agents Still Aren't Ready
For all the bold promises from tech CEOs about AI agents “joining the workforce” and driving “multi-trillion-dollar opportunities,” the reality is far less inspiring. What we currently have are not autonomous agents, but glorified chatbots dressed in fancy packaging; mostly mimicking scripts. Give them the same task twice, and you’ll often get wildly different results.
AI Identities: The Role of Agentic Systems in Governance
Agentic AI systems are rapidly emerging in enterprise environments. To govern them safely, each agent needs to be recognized as a first-class identity with its own credentials, permissions, and audit trail.
Best 17 AgentOps Tools: AgentNeo, Langfuse & more
We will introduce leading AgentOps tools, outline the challenges of operating agents and explain how an AgentOps automation pipeline can address them through observability, metrics, issue detection. How to think about AgentOps One of the hard parts of operating reliable agentic systems is making sure system behavior is observable and traceable at every step.
Agents.md: The README for Your AI Coding Agents
This guide introduces AGENTS.md, an open specification changing how AI agents interact with software projects. Unlike traditional README.md files, which are written for humans and often leave gaps, AGENTS.md provides clear, machine-readable instructions that both people and AI agents can follow with precision.
The 7 Layers of Agentic AI Stack
The rise of agentic AI has introduced a technology stack that extends well beyond simple calls to foundation-model APIs. Unlike traditional software stacks, where value often concentrates at the application tier, the agentic AI stack distributes value more unevenly. Some layers offer strong opportunities for differentiation and moat building, while others are rapidly becoming commoditized.
Agentic Mesh: The Future of Scalable AI Collaboration
While much has been written about agent architectures, real-world production-grade implementations remain limited. Building on my earlier post about A2A fundamentals, this piece highlights the agentic AI mesh, a concept introduced in a recent McKinsey.
MCP Security: Best Practices and Avoid Common Pitfalls
The model context protocol (MCP), pioneered by Anthropic, is quickly becoming the go-to standard for connecting large language models (LLMs) to the outside world. But the same simplicity that makes MCP so powerful also makes it risky.
Building a No-Code AI Lead Generation Workflow with n8n
I have been reviewing popular AI sales agents, including AiSDR and Outreach.io. While these platforms support lead management, they are typically focused on broader sales engagement and delivered as commercial packages with costs ranging from $2K to $5K per user per month.
Top 15 AI Agent Observability Tools: Langfuse, Arize & More
Observability tools for AI agents, like Langfuse and Arize, help gather detailed traces (a record of the processing of a program or transaction) and provide dashboards to track metrics in real-time. Many agent frameworks, like LangChain, use the OpenTelemetry standard to share metadata with observability tools.
AI Apps with MCP Memory Benchmark & Tutorial
We compared various memory tools using LangChain’s ReAct Agent and four different Model Context Protocol memory servers to determine which performs best. Also, we explored how to integrate Claude with Cursor to implement context-aware shared memory with OpenMemory MCP. This integration allowed us to demonstrate how memory is retrieved and managed in real-time.
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