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Over the last few months, tools like OpenClaw have shown what tech-savvy AI users can do by setting a virtual cadre of automated agents on a task. But that individual convenience can be a DDOS-level pain for online service providers faced with a torrent of Sybil attack-style requests from thousands of such agents at once. […]
How World ID wants to put a unique human identity on every AI agent Read More »
Struggling to make AI systems reliable and consistent? Many teams face the same problem. A powerful LLM gives great results, but a cheaper model often fails on the same task. This makes production systems hard to scale. Harness engineering offers a solution. Instead of changing the model, you build a system around it. You use prompts, tools, middleware, and evaluation to guide the model toward reliable outputs. In this article, I have built a reliable AI coding agent using LangChain’s DeepAgents and LangSmith. We also test its performance using standard benchmarks. What is Harness Engineering? Harness engineering focuses on building a structured system around an LLM to improve reliability. Instead of changing the model itself, you control the environment in which it operates. A typical harness includes a system prompt, tools or APIs, a testing setup, and middleware that guide the model’s behavior. The goal is simple: improve task success and manage costs while using the same underlying model. In this tutorial, we use LangChain’s DeepAgents library to demonstrate this approach. DeepAgents acts as an agent harness with built-in capabilities such as task planning (to-do lists), an in-memory virtual file system, and sub-agent spawning. These features help structure the agent’s workflow and make the system more reliable. Also Read: A Guide to LangGraph and LangSmith for Building AI Agents Evaluation and Metrics To evaluate the system, we need clear performance metrics. In this tutorial, we build a coding agent and test it using the HumanEval benchmark. HumanEval consists of 164 hand-crafted Python problems designed to evaluate functional correctness. We use two common evaluation metrics: Building a Coding Agent with Harness Engineering We will build a coding agent and evaluate it on benchmarks and metrics that we will define. The agent will be implemented using the deepagents library by LangChain and
Harness Engineering with LangChain DeepAgents and LangSmith Read More »
In this tutorial, we build an enterprise-grade AI governance system using OpenClaw and Python. We start by setting up the OpenClaw runtime and launching the OpenClaw Gateway so that our Python environment can interact with a real agent through the OpenClaw API. We then design a governance layer that classifies requests based on risk, enforces
OpenViking is an open-source Context Database for AI Agents from Volcengine. The project is built around a simple architectural concept: agent systems should not treat context as a flat collection of text chunks. Instead, OpenViking organizes context through a file system paradigm, with the goal of making memory, resources, and skills manageable through a unified
Most LLM agents work well for short tool-calling loops but start to break down when the task becomes multi-step, stateful, and artifact-heavy. LangChain’s Deep Agents is designed for that gap. The project is described by LangChain as an ‘agent harness‘: a standalone library built on top of LangChain’s agent building blocks and powered by the
What if AI-assisted coding became more reliable by separating product planning, engineering review, release, and QA into distinct operating modes? That is the idea behind Garry Tan’s gstack, an open-source toolkit that packages Claude Code into 8 opinionated workflow skills backed by a persistent browser runtime. The tookit describes itself as ‘Eight opinionated workflow skills
Google DeepMind team has introduced Aletheia, a specialized AI agent designed to bridge the gap between competition-level math and professional research. While models achieved gold-medal standards at the 2025 International Mathematical Olympiad (IMO), research requires navigating vast literature and constructing long-horizon proofs. Aletheia solves this by iteratively generating, verifying, and revising solutions in natural language.
Most AI tools forget you as soon as you close the browser window. The system begins all interactions with a new user. AI agents provide a solution to this problem because they handle their complete workflow through their system. MaxClaw is one of the best in this category. MiniMax developed this system which operates completely from the cloud space. The system
A Beginner’s Guide to Building Autonomous AI Agents with MaxClaw Read More »
In recent times, many developments in the agent ecosystem have focused on enabling AI agents to interact with external tools and access domain-specific knowledge more effectively. Two common approaches that have emerged are skills and MCPs. While they may appear similar at first, they differ in how they are set up, how they execute tasks,
Stanford researchers have introduced OpenJarvis, an open-source framework for building personal AI agents that run entirely on-device. The project comes from Stanford’s Scaling Intelligence Lab and is presented as both a research platform and deployment-ready infrastructure for local-first AI systems. Its focus is not only model execution, but also the broader software stack required to
