LLM

Designing Agent Workflows: Turning Language Models into Reliable Doers

The hard part of agents is not tool calling. It is recovery. Models plan, tools execute, and the system must absorb mistakes without collaps

ivhen

14 Sep 2025

The hard part of agents is not tool calling. It is recovery. Models plan, tools execute, and the system must absorb mistakes without collapsing into confusion.

Enterprise agents usually work better with semi-structured workflows than with total freedom. State persistence, execution history, and rollback logic matter far more than whether the model sounds impressive in a demo.

Plenty of models can talk. Very few agents can finish work reliably.

LLM System Thinking: Product, Engineering, and Cost Must All Work Together

Many AI projects focus on isolated model quality and ignore the system around it. A better answer does not automatically mean a better produ

Python AI Tooling: Building Model-Powered Tools Developers Will Actually Use

AI tooling becomes valuable when it enters everyday developer workflows, not when it only shines in a demo window. Python is an ideal layer

Java Governance Platforms: Converting Experience into Shared Capability

Too much engineering governance depends on a few experienced people remembering the right risks at the right time. Platform thinking changes

LLM Evaluation Loops: No Long-Term Control Without Measurement

Many language model projects slow down after launch because they never build a real evaluation loop. A system that looks impressive in a sma

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LLM System Thinking: Product, Engineering, and Cost Must All Work Together

Python AI Tooling: Building Model-Powered Tools Developers Will Actually Use

Java Governance Platforms: Converting Experience into Shared Capability

LLM Evaluation Loops: No Long-Term Control Without Measurement