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OpenAI's finance team shares lessons from building an AI-native finance function, emphasizing broad access, hackathon-driven experimentation, and redesigning workflows around decisions using tools like IR-GPT.
The article describes an internal Agent Run Dashboard that tracks AI agent runs, quality ratings, and workflow performance, giving visibility into human-AI collaboration and suggesting the approach can be applied beyond software development.
The author shares frustration with duct-taping data prep tools for AI agents and proposes a simpler approach: upload raw files, describe the desired output in plain English, and receive cleaned, structured data ready for agents.
This paper presents CAi Copilot, an expert-oriented agent with three linked layers that turns molecular design intent into executable, traceable workflows, achieving the strongest performance across 45 tasks.
OpenAI highlights how Zapier's enterprise marketing team uses ChatGPT Work to automate lead QA/QC, freeing up time for strategic work and delivering significant pipeline impact.
The author contrasts AI sidekicks with autonomous background agents, arguing that background agents deliver 10x more enterprise value but are far harder to build due to workflow re-engineering and limited AI talent.
An automation consultant warns that automating workflows without understanding their original purpose can encode outdated rituals and lost context, telling a story of a meaningless 24-hour hold that survived a retired batch system.
The author argues that many research agent projects can be replaced by 'skills' running inside existing harnesses like Codex, sharing their experience implementing a Reddit customer-research workflow as a Codex skill with only deterministic Python helpers, and questioning when custom agent runtimes are truly needed.
Agentic Harness is a new open-source Python infrastructure that turns YAML files into executable agent workflows, featuring markdown audit logs, pre-built specialists, cost guardrails, native MCP support, and local model integration.
A new AI agent system uses eight specialized agents to autonomously discover, validate, and integrate new skills from GitHub, requiring only final human approval before merging.
n8n now officially supports Alibaba Cloud and Qwen models via Bring Your Own API Key, enabling secure enterprise data connections, complex agent orchestration, and automated SaaS decision-making in workflows.
Adeptly is a free, open-source tool that teaches users how to use more of Claude Code's features by baking them into project plans. Version 0.5 adds an automated pipeline of roles (Architect, Builder, Medic, etc.) that executes workflows locally, with dry-run mode and a safety flag to prevent accidental live runs.
Oh-my-hermes (OMH) is a tool that enhances Hermes Agent with a stronger operating layer, adding planning, research, creation, coding handoffs, operations, and project memory with explicit evidence boundaries. It is open-source and installable with a single command.
A team used AI to automate a manual document sorting process, reducing labor from 50-70 hours to 3-5 hours per month by grouping scanned pages into documents and generating PDFs.
Mach 1 built an AI agent platform that uses Zapier MCP to connect to 25 companies' diverse tool stacks, completing over 150k tasks across internal workflows and customer operations.
QwenPaw 2.0.1 introduces new features: PawApp for building interactive agent apps, Kanban for tracking agent status, customizable Agent Loops, 5 ready-to-use workflows via Oh-My-Paw, and enhanced security.
A practitioner shares their experience with MCP (Model Context Protocol) servers for business work, detailing which ones provide real read/write capabilities (e.g., Postgres MCP, HubSpot MCP, PostFast) and which disappoint (e.g., Slack MCP, Google Ads MCP), while highlighting major security concerns like low OAuth adoption and high vulnerability rates.
Intel's experiments offer five practical lessons for enterprise leaders building infrastructure for agentic AI, emphasizing that it is a systems problem beyond inference and providing metrics like agent density and task latency for effective deployment.
Aaron Levie discusses the need for an applied AI layer to bridge AI model breakthroughs with enterprise workflows, emphasizing that as models improve, more ambitious automation becomes possible, creating ongoing opportunities for specialized companies.
The author discusses the challenge of communicating automation results to non-technical clients and proposes building a white-label monthly report tool that summarizes runs, hours saved, and issues, with alerts for silent workflow failures. Feedback is sought on the idea and willingness to pay $49/month.