The Stellary Product Desk writes about delivery systems, board structure, cockpit-style piloting, documentation practices, and software evaluation criteria for product and engineering teams. It specializes in trade-offs, workflow design, and adoption patterns.
About this desk
Documents real delivery workflows, planning systems, workspace design, and how product teams operate with AI in the loop.
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These articles are currently owned or maintained by this editorial desk.
Compare the documented agent capabilities of Linear, ClickUp, Notion, Asana, monday, and Stellary across one reproducible sprint benchmark.
An AI project brief connects outcomes, scope, sources, constraints, and validation. Use this template before assigning work to an agent.
Task boundaries, context, worktrees, contracts, and integration: a method for running multiple AI coding agents in parallel without multiplying conflicts.
Astra or Fable 5.1? Compare capabilities, pricing, context, safeguards, and agentic workflows to choose the right coding model.
Vibe coding accelerates prototyping. Use this practical method to secure architecture, data, testing, and deployment before going to production.
Building a video game with AI takes more than code. Follow a practical method to organize mechanics, assets, tests, decisions, and agents in one project.
Who sits on the steering committee of a project that runs AI agents, at what cadence, with which agenda — and the extra question it has to ask every week.
A task tool is not a steering tool. The six capabilities to require, how Jira, Linear, monday, Notion and ClickUp fare, and the agent-native option.
How to steer a programme (several linked projects) with AI agents: portfolio view, dependencies, trade-offs prepared by the agent, human decisions on record.
A steering dashboard does not reassure, it triggers decisions. The eight widgets that matter, the ones to remove, and how to compose them without double entry.
A worked example and a reusable prompt for turning client emails into tasks, keeping source links, spotting missing details and checking deadlines.
Your AI agent says the job is done. Learn how to check the actual result, find supporting evidence and catch missing work before you sign it off.
Simple examples of how AI can help in administration, sales, healthcare, education, creative work, engineering, and field-based professions.
Low, medium, high, or maximum: learn how to choose the right AI reasoning effort without wasting time or budget.
A simple guide to choosing local, cloud, or hybrid AI based on your documents, budget, internet connection, and everyday needs.
Fable 5.1 or Opus 5? Choose by mission difficulty and cost per successful task — not by putting Anthropic’s strongest model everywhere.
Sol, Terra, or Luna? Pick GPT-5.6 by mission difficulty, volume, and cost per successful task — not by putting the strongest model everywhere.
MCP or REST API for an AI agent? Choose based on who picks the operation: the code, the model, or both in a hybrid architecture.
Choose between automation, an AI workflow, an agentic workflow, and a bounded autonomous agent based on who picks the next step, cost, latency, and risk.
AI model fusion: multi-agent pipelines combine several models, then compare and judge their answers to deliver a more robust result than a single model alone.
Why documentation and memory are the two foundations of reliable AI agents: context, RAG, governance, trust, and practical habits for modern teams.
Understand AI project management in 2026: assistants, automations, operational agents, governance, MCP, benefits, risks, and a practical adoption plan.
Learn how AI-assisted project piloting connects goals, delivery signals, decisions, agents, and controlled actions without replacing human accountability.
Why dev teams lose critical knowledge and how to build a system that keeps documentation alive, searchable, and connected to your workflow.
Practical strategies for using AI to bridge the gap in distributed teams — from async decision-making to automated standups and intelligent notifications.
Use AI to prepare Sprint Planning evidence, surface dependencies and compare capacity scenarios while the Scrum Team keeps the Sprint Goal and forecast.
Compare Scrum, Kanban, and hybrid delivery in 2026, then see where AI can prepare evidence without replacing goals, flow policies, or team judgment.
Use AI standups, decision briefs, and project signals to replace status-only meetings while keeping the conversations that need real-time human judgment.