Tag: Microsoft Copilot

  • How I Keep Copilot Cowork Sessions Alive with a requirements.md File

    How I Keep Copilot Cowork Sessions Alive with a requirements.md File

    Copilot Cowork is strong at creating files. Documents, markdown files, HTML files, specs, plans, summaries, all of it.

    So I started using that strength against one of the biggest pain points in agent work: losing context.

    1. The Problem
    2. The Workaround
    3. The Prompt I Use
    4. What I Want Cowork To Track
    5. The Recovery Flow
      1. Step 1: Open the Output Folder
      2. Step 2: Copy the OneDrive Link
      3. Step 3: Start a New Cowork Session
    6. Why This Works
    7. My Recommendation
    8. Final Take

    The Problem

    Sometimes a session can glitch, freeze, or reach a point where starting fresh is easier.

    The painful part is not starting a new session.

    The painful part is rebuilding the context.

    You have to explain the goal again. Rebuild the requirements. Re-upload or reconnect files. Remind it what decisions were already made. Recreate the mental map of the work.

    That burns time.

    So I started giving Copilot Cowork a job before it does any other job:

    Keep the context alive.

    The Workaround

    At the start of the session, tell Cowork to create a requirements.md file and keep it updated while you work.

    That file becomes the session brain.

    It gives you a portable record of the work that can move from one Cowork session to another.

    Think of it like a handoff file.

    Not a final deliverable. Not a pretty summary. A working memory file.

    The Prompt I Use

    Create a requirements.md file and keep it updated throughout this session.
    Use it to track the full context of our work, including:
    - requirements
    - decisions made
    - open items
    - files created
    - key conversation details
    - risks
    - assumptions
    - and next steps
    I want to be able to pass this file to another Copilot Cowork session
    so it can continue with full context.

    You can change the file name if you want.

    For some sessions, I might use project-context.md, demo-notes.md, or handoff.md.

    But I like requirements.md because it forces the session to stay grounded in what is actually being built.

    Note

    This works best when the requirements.md file is updated throughout the session, not only at the end. When decisions change, files are created, or blockers appear, tell Cowork to update the file.

    What I Want Cowork To Track

    The file should not be a fluffy recap.

    I want it tracking the stuff that matters:

    • Session goal
    • Current objective
    • Requirements
    • Decisions made
    • Files created
    • Important assumptions
    • Open questions
    • Risks or blockers
    • Next actions
    • Anything another session would need to continue the work

    That last one is the key.

    Do not just ask Cowork to summarize.

    Ask it to prepare the next session to continue the work.

    The Recovery Flow

    If the session glitches, breaks, or you want to continue in a fresh session, here is the flow I use.

    Step 1: Open the Output Folder

    In Copilot Cowork, open the details pane and look for the Output folder.

    Click the folder icon to open the generated files in OneDrive.

    Once the folder opens in OneDrive, click Copy link.

    This gives you a link to the folder that contains the files from the previous Cowork session.

    Step 3: Start a New Cowork Session

    Open a new Copilot Cowork session.

    Paste the OneDrive folder link into the new session and tell Cowork:

    Im continuing the <Project or task name>.
    Use the files from our previous session. ( <Paste OneDrive Link> )
    Start by reading the requirements.md file.
    Then continue the work from there.

    Now Cowork has a fighting chance at picking up where the previous session left off.

    Why This Works

    Agent workflows are only as strong as the context behind them.

    If the context is trapped inside one chat session, you are exposed.

    If the context is written into a file, you can move it.

    That changes the way you work with Cowork.

    You are no longer relying only on the chat thread.

    You are creating a portable project trail that can survive a new session.

    My Recommendation

    Make this part of your normal Copilot Cowork workflow.

    Before you ask it to build the document, analyze the data, write the plan, or generate the assets, tell it to create the context file first.

    Then keep pushing Cowork to update that file as the session evolves.

    When a decision is made, tell it to update the file.

    When a requirement changes, tell it to update the file.

    When a file is created, tell it to update the file.

    Small habit.

    Big protection.

    Final Take

    Copilot Cowork can generate the work.

    But you should also make it generate the trail.

    The requirements file keeps the important context outside the chat window, inside the actual working folder, where another session can use it.

    That is the move.

    Use Cowork to build the output.

    Use Cowork to protect the context.

    This is currently a limitation on the product, which I assume the Team will fix in the future. But for now, this is how I manage long running tasks and work with Copilot Cowork.

  • Boost Productivity with Planner and Copilot Cowork

    Boost Productivity with Planner and Copilot Cowork

    Copilot Cowork Prompt of the Day: Planner + Cowork

    Most people open Planner, see a wall of tasks, and then burn time figuring out where to start.

    Bad move.

    A better play is to use the Planner (Frontier) agent to pull everything due this week, then hand that full output to Copilot Cowork so it can start moving the work immediately.

    That is exactly what I did here.

    I had 6 tasks due in the upcoming week. Instead of manually opening each one, rewriting the context, drafting emails, setting meetings, and creating documents one by one, I used Planner to extract the full list and Copilot Cowork to help kick things off.

    The scenario

    I had a packed week ahead with multiple tasks already assigned to me in Planner. I needed a fast way to get organized and get the work moving before the week started tightening up.

    So I used the new Planner (Frontier) agent to give me a full view of what was due, then I passed that output directly into Copilot Cowork.

    That gave Cowork real context to work with: due dates, notes, priorities, task titles, and the actual work that needed to happen.

    Step 1: Pull the weekly task list from Planner

    First, I asked Planner to show me everything due this week and give me the full details.

    Show me tasks that are due this week.
    Give me full details.
    I will be giving your output to Copilot Cowork to do the tasks.

    Planner returned the task list with the details I needed. Then I asked it for one more thing: put the output into a code block.

    Can you output this in code block

    That extra step is smart. It makes the output much easier to copy and paste cleanly without wrecking the formatting or losing structure. Also AI loves it.

    Step 2: Hand the extract to Copilot Cowork

    Next, I dropped that Planner output into Copilot Cowork with a very simple prompt.

    I pulled the list of items I need to get done this week coming up.
    Get these things going and started, so I can get them done on time.
    Here is the extract from Planner:
    <Paste output from Planner agent from step 1>

    No complicated framework. No giant system prompt. Just the actual task extract from Planner and a direct ask.

    That is where this gets good. Cowork is no longer guessing what matters. It has the real list in front of it.

    Step 3: Let Cowork start the work

    Once Cowork had the context, it started breaking the workload down and pushing the execution forward.

    In my example, the work included:

    • Drafting emails and Teams messages to align what was still left for the team to build
    • Setting up workshop meetings to gather more requirements
    • Checking availability and scheduling the right people
    • Creating a procedure document
    • Helping move general dev work forward

    That is a strong workflow because it cuts out the dead time between seeing the work and starting the work.

    Instead of looking at six tasks and thinking, I should probably get on this, Cowork starts lining up the actual actions that move those tasks forward.

    Teams message sent to group chat:


    Procedure document generated:

    Meeting scheduled:

    Recap

    Planner and Cowork each do a different job, and that is exactly why this combo works.

    • Planner gives you the structured workload
    • Planner gives you the dates, notes, priority, and task details
    • Cowork uses that context to help draft, schedule, create, and coordinate

    You are not starting from a blank chat. You are starting from actual work that already exists inside Microsoft 365.

    Prompt recap

    Planner prompt

    Show me tasks that are due this week.
    Give me full details.
    I will be giving your output to Copilot Cowork to do the tasks.

    Planner follow-up

    Can you output this in code block

    Copilot Cowork prompt

    I pulled the list of items I need to get done this week coming up.
    Get these things going and started, so I can get them done on time.
    Here is the extract from Planner:
    <Paste output from Planner agent from step 1>

    Why I like this prompt

    This one is simple, practical, and dangerous in a good way.

    You take a task list that usually just sits there, and you turn it into momentum.

    That is the real win. Less dragging. More movement.

    If you are already using Planner, this is one of the easiest Copilot Cowork workflows to start using right now.

  • Build Your Agent Factory: 10 Moves That Ship Fast (and Scale)

    Build Your Agent Factory: 10 Moves That Ship Fast (and Scale)

    Build Your Agent Factory: 10 Moves That Ship Fast (and Scale)

    Agents at scale. Not POCs.

    Here’s the playbook I’d hand any exec or builder who wants working agents in production—without turning the org into a science fair.

    1) Stand up an AI Agents Workforce

    What it is: A small cross-functional crew with authority to hunt repetitive work and ship agents.

    Who’s in:

    • 1 product owner
    • 1 engineer (Copilot Studio/Power Automate)
    • 1 data person
    • 1 security/governance lead
    • 1 domain SME.

    Ship this week: Write a one-page charter with scope, decision rights, and a 30-day roadmap (first 5 agents + metrics).

    2) Win with horizontals first, then go vertical

    Horizontals (1-hour wins): drafting, summarizing, policy Q&A, meeting notes to actions, form-fill helpers.

    Verticals (outsized ROI): pick 1–2 per business unit where there’s money, risk, or SLA pain.

    Guardrail: don’t start with the hardest workflow; start where you can close the loop and measure value inside two weeks.

    3) Make an Agents Directory the front door

    Why: Ideas die in email. A directory turns “we should build X” into spec and governance.

    Minimum intake fields:

    • use case name
    • goal
    • users
    • decision rights
    • data sources + who owns it
    • tools
    • PII/sensitivity
    • KPIs
    • business owner
    • risk level
    • rollout plan.

    Outcome: Every request auto-generates a lightweight PRD (goal, inputs, outputs, metrics, guardrails) and a yes/no gate.

    4) Create the 1-Hour Agent template

    Template anatomy:

    Goal + success criteria Input schema (what the user provides) Tools (actions/connectors) and permissions Knowledge sources (files, sites, indexes) Safety rules (allowed/blocked actions, escalation) Evaluation set (10–20 test prompts with expected outcomes) Deploy script (Dev → Test → Prod)

    Rule: If a use case can’t fit this page, it’s not a 1-hour agent—park it for later.

    5) Tie every agent to a visible scorecard

    Metrics to publish: time saved, cost avoided, error rate, CO₂/efficiency (where relevant), user satisfaction.

    Simple formula: monthly users × average minutes saved × loaded cost = value.

    Make it public internally: green/red status, owner, last review, next improvement.

    6) Run on a secure, managed agent runtime

    Non-negotiables: identity passthrough, content safety, audit logs, tool call restrictions, data boundary controls, environment isolation.

    Practical tip: standardize a “sensitive sources” policy and block tools by default; allow case-by-case.

    7) Split the stack to move fast without breaking things

    Experience layer: Copilot Studio for UX, channels, and connectors.

    Agent runtime/orchestration: managed agent service for threads, tool calls, safety, and evaluations.

    Why it works: builders ship quickly at the edge; platform team keeps shared guardrails, monitoring, and upgrades stable.

    8) Mix knowledge + action (or you’ll stall)

    Knowledge: structured grounding (SharePoint/Fabric/Search), doc versioning, citations-on by default.

    Action: flows/Logic Apps, Graph, line-of-business APIs; always ship with a dry-run mode first.

    Design pattern: Answer → show sources → propose actions → execute on approval. When confidence is high and stakes are low, allow auto-execute.

    9) Keep humans in the loop—by design

    HITL patterns that work:

    Shadow mode (observe only) → suggest mode → execute with approval → auto-execute.

    Confidence thresholds where low confidence routes to a human. Escalation logic when guardrails trip or data is missing.

    UX rule: one click to approve, one click to undo.

    10) Plan to scale on day one

    Pipelines: Dev → Test → Prod with approvals and rollback.

    Evals: pre-ship test set per agent; weekly drift checks; quarterly red-team.

    Ops: central logging, cost dashboards, incident playbook.

    Program ritual: a quarterly “Agent Backlog Day” to harvest new ideas and retire underperformers.

    Starter Architecture (fast and boring)

    Experience: Copilot Studio (web, Teams, M365, chat, plugins)

    Actions: Power Automate/Logic Apps + custom APIs

    Knowledge: SharePoint/Fabric/AI Search with retrieval policies

    Runtime: managed agent service for tool orchestration, identity, safety

    Observability: evaluations, telemetry, and a simple agent scorecard per app

    Security: Entra ID RBAC, private endpoints, DLP, approval gates

    Prompts and policies that save you pain

    Prompt contract (keep it in the repo): role, goals, inputs, allowed tools, forbidden actions, decision rights, escalation, output format, citation rules.

    Data contract: what sources are permitted, freshness expectations, sensitivity tags.

    Failure modes: what the agent must do when unsure (ask for clarification, route to human, or stop).

    Anti-patterns I keep seeing

    • Starting with an “AI strategy deck” instead of shipping 3 agents.
    • Agents that answer but can’t act—users stop coming back.
    • No owner, no scorecard, no sunset date.
    • Canary-testing in production without a rollback plan.
    • Letting one giant use case block 20 small wins.

    Your first week mapped

    Day 1: Form the team and publish the charter.

    Day 2: Launch the Agents Directory (intake + PRD autogeneration).

    Day 3–4: Build two 1-hour agents (drafting + policy Q&A) with eval sets.

    Day 5: Ship to a pilot group with scorecards visible. Book the first backlog day.