Agent Control Patterns — Part 4: ReAct — Thinking While Acting
Author(s): Vahe Sahakyan Originally published on Towards AI. So far in this series, we designed systems with predefined structure. We defined the execution flow. We separated reasoning from revision. We controlled when research happens. Even when loops were introduced, the workflow remained …
Agent Control Patterns — Part 3: Reflexion — When Review Triggers Research
Author(s): Vahe Sahakyan Originally published on Towards AI. A model can review its own answer and still return incorrect information. It may recognize uncertainty, improve its wording, and clarify its reasoning. But if the missing piece is factual, reviewing the answer again …
Agent Control Patterns — Part 2: Reflection — A Simple Way to Improve Answer Quality
Author(s): Vahe Sahakyan Originally published on Towards AI. An agent can execute a workflow correctly and still produce a weak answer. It may follow the graph, respect routing rules, and stop exactly where it should — yet return a response that lacks …
Where LLMs Belong in Agentic Systems: Gating, Approval, and Human-in-the-Loop Design
Author(s): Vahe Sahakyan Originally published on Towards AI. This article closes a four-part series on designing agentic AI systems. So far, we’ve focused on structure first. We separated agentic behavior from language models. We built workflows with explicit control flow, shared state, …
Building an Agentic Workflow in LangGraph (No LLM Required)
Author(s): Vahe Sahakyan Originally published on Towards AI. Most introductions to agentic AI start with a language model. This one doesn’t. That choice is deliberate. If you begin with an LLM, it’s easy to mistake intelligence for agency. You end up tuning …
LangChain vs LangGraph: Pipelines vs Processes in Agentic Systems
Author(s): Vahe Sahakyan Originally published on Towards AI. The first time you try to build a real agentic system, something breaks. Not a bug.An assumption. At first, everything looks fine. You chain together a few steps. The model reasons. A tool gets …
From Questions to Insights: Data Analysis with LangChain’s Built-In Tools
Author(s): Vahe Sahakyan Originally published on Towards AI. In the first two articles of this series, we established why tools are essential for agentic systems and how those tools are constructed and orchestrated inside agents. What we deliberately avoided until now is …
How Tools Turn into Agents: What Actually Happens at Runtime
Author(s): Vahe Sahakyan Originally published on Towards AI. Many AI agent demos look convincing — until they fail in practice. Tools are defined correctly. Prompts seem reasonable. Yet the agent either calls the wrong tool, fails to call any tool at all, …
The Hard Limit of Prompting — and Why AI Agents Need Tools
Author(s): Vahe Sahakyan Originally published on Towards AI. People often believe that better prompts will eventually make AI agents reliable. They won’t. You can instruct a language model to “double-check its work,” “reason step by step,” or “be precise” — but none …
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