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A new workflow allows developers to perform the full agent development lifecycle (build, test, deploy, evaluate, govern) directly from within their coding agent, using Agents CLI and Agent Development Kit.
LangChain announces a summer AMA series on the agent development lifecycle using LangSmith, covering building, deploying, monitoring, improving, and governing agents.
The Harness Handbook is a behavior-centric representation synthesized from agent harness codebases using static program analysis and LLM assistance, helping developers and coding agents locate code implementing specific behaviors. It introduces Behavior-Guided Progressive Disclosure (BGPD) to guide agents from high-level descriptions to relevant implementation details, improving localization accuracy and edit-plan quality.
The author presents a pattern for sharing AI agents by compiling them into sealed WASM modules that only import a single inference function, allowing others to run the agent using their own local model without API keys or security risks.
A tweet thread discussing the iterative process of building evaluations and environments for AI agents, emphasizing human-agent collaboration and the importance of data and verifier design.
Superpowers 6 is released, using Fable 5 to run 25 autonomous experiments, improving build speed by 50% and reducing token costs by 60%, with detailed records of the experimental process and lessons from failures.
The University of Hong Kong Data Intelligence Lab has open-sourced the lightweight agent framework OpenHarness and its built-in agent Ohmo, providing core features like tool calling, memory, multi-agent coordination, and support for platforms like Feishu and Slack.
A user shares positive impressions of the GLM-5.2 model, noting its impressive performance and cost-efficiency compared to Deepseek, and reflects on the educational value and limitations of AI agents for coding and learning.
Discusses the common problem of AI agents performing well in development but failing with real user phrasing, asking how developers bridge this gap.
At the Generative AI Developer Conference, ihower shared about the Harness + Loop Engineering series of articles and slides for Agent development.
The author shares five patterns that consistently kill AI agents: too many jobs per agent, no human-in-the-loop for destructive actions, unstructured outputs, no spend caps, and lack of uncertainty escalation paths. Practical guardrails and a checklist for reliable agent deployment are provided.
A comprehensive guide to building AI agent harnesses, covering tool execution, context management, state/memory, and guardrails, based on lessons from building Claude Code and other harnesses for enterprise.
Harrison Chase shares insights on the Agent Development Cycle, emphasizing that shipping the first version is only the beginning and that a repeatable lifecycle is key to building reliable agents.
An experiment applying BEAM-style concurrency (Erlang VM model) to coding agents yielded surprising results, suggesting potential improvements in agent coordination and fault tolerance.
The author argues that many human approval gates for AI agents are ineffective rubber stamps, and proposes a framework for designing meaningful review mechanisms that actually catch errors.
People want to build agents easily and have them improve over time; automating the 'hill climbing' loop is hard but has high ROI.
Google Devs introduces Agent Factory series with ADK 2.0 and Gemini 3.5 Flash, demonstrating how to build production-grade stateful agents that can run for days, featuring insights on skills, MCP, and code review strategies from engineers like Rohde Davis.
The article outlines a five-step workflow for spec-driven development using Warp skills: write a product spec (PRODUCT.md), write a tech spec (TECH.md), implement with any AI agent, validate implementation matches specs, and validate using computer use with Oz. The skills are open-sourced and can be installed via npx.
The author argues that AI agents in production should be defined as declarative manifests with their own runtime, rather than being scattered across application code, in order to enable proper versioning, observability, and rollback. They present their own solution as an open-source tool.
This article introduces the concept of 'Harness Engineering,' a discipline focused on designing the systems that constrain and guide AI agents to make them reliable in production, arguing that the harness matters more than the model itself.