agent-workflow

The author describes using Mastra's new preview deployment feature to let their coding agent automatically deploy changes to a sandbox, test them via API and UI, and then open a PR, closing the verification gap.

A team benchmarked routing different stages of an AI agent workflow to different models versus sending every request to Claude Opus 5 across 89 Terminal-Bench 2.1 tasks, and found surprising results.

The author created an open-source tool called awman that implements Claude's dynamic workflows concept, allowing multiple agents/models to collaborate on tasks with features like leader-designed workflows, shared context, and automated remediation.

A plugin distills Fable's problem-solving approach into a structured loop with verification agents, improving model reliability on research tasks and error detection, especially for weaker models.

Someone has organized the entire agent workflow of Fable 5 into 9 images, which can be directly fed to Claude for use, making it convenient and efficient.

Hermes Agent 0.18.0 Runtime is now officially live, with new /moa and /learn instructions, improving combined model calling and learning context capabilities.

The author introduces Dagu as a lightweight alternative to Temporal for dynamic workflow orchestration, along with a promotional plug for the Northflank platform.

Argues that the key skill for product managers in the AI era is loop engineering, not prompt engineering. Describes how to create reusable, self-improving loops for AI agents to maintain quality and avoid drift.

Maka has released Autonomous Task Loop v1, enabling a persistent agent loop: preflight → runtime → SelfCheck → FeedbackObservation → Decision. It supports self-checking, budget control, and state recovery, giving Maka's desktop AI workstation the foundational ability to run ongoing tasks.

This article proposes a 14-step roadmap from single agent to self-evolving system, emphasizing that base engineering (models, tools, permissions, context) is the key to determining the quality of loop output, and details practical methods for building an efficient base such as CLAUDE.md, sub-agents, skills, hooks, and state files.

A developer built Theodosia, a tool that uses a state machine as an MCP adapter to enforce legal transitions in agent workflows, preventing incorrect completions and providing a hash-chained ledger of steps.

This post demonstrates how to fine-tune a model for free using a single prompt, leveraging the new Google Colab CLI along with Hugging Face's TRL and trackio tools, all orchestrated by an AI agent.

Claude Code v2.1.172 adds sub-agent nesting capability, supporting up to 5 layers of nesting. It allows lower-level agents to automatically generate sub-agents to handle complex sub-tasks, and introduces usage scenarios, configuration methods, and common pitfalls.

An observation that two instances of the same AI model on the same task can produce different internal behavior (e.g., one refactoring a shared utility while the other does not), highlighting the challenge of reviewing agent work by final output alone.

An open-source plugin introduces an audit-first workflow for AI coding agents converting web apps to native mobile apps, using a structured Markdown plan and approval gates to avoid premature coding.

A tool for visualizing AI agent workflows is introduced, supporting multiple agent frameworks including Langgraph, CrewAI, AutoGen, Google ADK, and OpenAI Agents SDK. The creator seeks community feedback and corrections.

Autocontext is an open-source recursive self-improvement harness that helps AI Agents continuously optimize through iterative execution, evaluation, and knowledge accumulation, generating reusable playbooks, datasets, and even local models. It is suitable for developers building production-grade Agent workflows.

MIT HAN Lab proposes a method to automatically design and optimize CUDA kernels using an AI agent workflow. Through a process of task contracts, agent loops, and small-step verification, the agent can autonomously iterate and optimize within a specialized toolchain, replacing manual tuning.

SkillHarm is a benchmark for evaluating skill-based attacks across the skill-use lifecycle, revealing high vulnerability (up to 86.3% attack success) in current AI agents and introducing automated attack construction via AutoSkillHarm.

The article highlights the growing problem of managing AI agent memory over time, where users spend more effort maintaining context than actually using the agent, and points out the lack of infrastructure for memory decay and governance.