Mert Palazoğlu
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
Authorization for AI Agents: Permit.io, Descope & more
I have been exploring agent identity and the authentication/authorization platforms that could support it, while also examining how standards like OAuth 2.0 and frameworks such as Keycloak might apply. Below, I listed the best AI agent–specific platforms and features, categorized by their primary focus.
How we Moved from LLM Scorers to Agentic Evals?
Evaluating LLM applications primarily focuses on testing an application end-to-end to ensure it performs consistently and reliably. We previously covered traditional text-based LLM evaluation methods like BLEU or ROUGE. Those classical reference-based NLP metrics are useful for tasks such as translation or summarization, where the goal is simply to match a reference output.
Top 40+ AI Developer Tools for Software Development
We have been experimenting with AI development tools in our code generation and code editing benchmarks for months. We have seen that AI agents like Claude Code are highly capable of software development, achieving ~%90 success rate.
Top 11 Open Source AI Platforms & Libraries
Deploying your own AI model or, in some cases, fine-tuning pre-existing models comes with several challenges: Open-source platforms that offer unified APIs help address these challenges by enabling multi-cloud deployment, optimizing GPU resource management.
The Industrial AI Agent Landscape: 30+ Platforms to Watch
Industrial AI agents address the limitations of siloed data by autonomously integrating and deriving actionable insights from IoT, controls systems (e.g. SCADA), and connected assets.
Agentic AI Architecture for Industrial Systems
Agentic AI allows natural language interaction with industrial systems, enabling users to query data and receive actionable insights. We will outline a reference architecture designed for industrial environments, describe how task-specific agents and tools can be orchestrated. We will also explore current state of natural language interfaces (NLIs) in industrial systems.
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.
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