update human review suggestions

Author: petender

learn-pr/wwl-azure/introduction-agentic-devops-microsoft-tools-azure/includes/3-map-devops-work-agentic-opportunities.md

@@ -63,9 +63,9 @@ For platform engineering teams, the optimization agent is particularly useful: i
 
 With opportunities across the entire DevOps lifecycle, you need a way to decide where to start. Apply three filters:
 
-1. **Frequency** — how often does this task occur? Daily tasks yield more accumulated time savings than monthly ones.
-2. **Context breadth** — how many sources does this task require you to consult? Tasks that require you to hold context from five places simultaneously are prime agents candidates.
-3. **Decision reversibility** — how easily can you undo the outcome? Tasks with reversible, low-blast-radius outcomes are better first-deployment candidates than high-stakes production operations.
+- **Frequency**: how often does this task occur? Daily tasks yield more accumulated time savings than monthly ones.
+- **Context breadth**: how many sources does this task require you to consult? Tasks that require you to hold context from five places simultaneously are prime agents candidates.
+- **Decision reversibility**: how easily can you undo the outcome? Tasks with reversible, low-blast-radius outcomes are better first-deployment candidates than high-stakes production operations.
 
 Pipeline failure triage scores high on all three. Production deployment authorization scores low on reversibility and should remain human-primary regardless of how mature your agentic practices become.
 

learn-pr/wwl-azure/introduction-agentic-devops-microsoft-tools-azure/includes/4-set-autonomy-boundaries-human-control.md

@@ -2,8 +2,8 @@ Identifying high-value agentic opportunities is the easier half of the problem.
 
 The engineers who get this concept wrong tend to fall into one of two failure modes:
 
-- **locked-down agents**: agents are locked so tightly, that the only value delivered is autocomplete-level suggestions. 
-- **autonomous execution**: agents run actions autonomously, without carefully analyzing impact.
+- **locked-down agents**: Agents are locked so tightly, that the only value delivered is autocomplete-level suggestions. 
+- **autonomous execution**: Agents run actions autonomously, without carefully analyzing impact.
 
 Both failures undermine adoption and erode trust. The goal is a calibrated model that matches autonomy level to reversibility, blast radius, and regulatory context.
 

learn-pr/wwl-azure/introduction-agentic-devops-microsoft-tools-azure/includes/5-exercise-classify-devops-backlog.md

@@ -10,9 +10,9 @@ You're evaluating which tasks to pilot with agentic capabilities in the next qua
 
 Review each task in the backlog below and assign:
 
-1. **Agentic opportunity?** — Yes or No
-2. **Autonomy level** — Inform, Suggest, Execute on approval, or Execute autonomously
-3. **Justification** — One to two sentences citing reversibility, blast radius, or compliance constraint
+- **Agentic opportunity?**: Yes or No
+- **Autonomy level**: Inform, Suggest, Execute on approval, or Execute autonomously
+- **Justification**: One to two sentences citing reversibility, blast radius, or compliance constraint
 
 | # | Task description | Frequency |
 |---|---|---|

learn-pr/wwl-azure/introduction-agentic-devops-microsoft-tools-azure/includes/7-summary.md

@@ -1,23 +1,23 @@
-Mature DevOps practice is built on the discipline of knowing which layer of your stack handles which class of problem. Agentic capabilities represent a new layer. It is not a replacement for your pipelines, scripts, or runbooks. Rather, it becomes a reasoning layer overtop of them that handles the context-intensive, judgment-requiring work your automation was never designed to absorb.
+Mature DevOps practice is built on the discipline of knowing which layer of your stack handles which class of problem. Agentic capabilities represent a new layer. It isn't a replacement for your pipelines, scripts, or runbooks. Rather, it becomes a reasoning layer overtop of them that handles the context-intensive, judgment-requiring work your automation was never designed to absorb.
 
 You learned how to define that layer. You mapped out where agentic AI delivers value across the DevOps lifecycle. Last, you established the autonomy boundaries that make it safe to deploy in production-grade Azure environments.
 
 ## Key concepts from this module
 
-- **Agents vs. automation**: Agents are goal-oriented, invoke tools dynamically, and adapt through observe-reason-act loops. This lets them handle complex, multi-step tasks requiring context synthesis instead of scripted steps.
+- **Agents vs. automation**: Agents are goal-oriented, invoke tools dynamically, and adapt through observe-reason-act loops. This approach lets them handle complex, multi-step tasks requiring context synthesis instead of scripted steps.
 - **Microsoft's agentic spectrum**: GitHub Copilot, Azure Copilot agents, Azure DevOps AI, and MCP extensibility provide agentic capabilities across different surfaces.
 - **High-value DevOps phases**: Plan/track, code review, pipeline triage, and observability are best suited for early adoption—they're frequent, information-rich, and low-risk.
-- **Autonomy matching**: Tie autonomy levels to reversibility and blast radius. High-risk operations (deployments, credentials, role assigments, policy changes) always require human approval.
-- **Observability from day one**: Log and audit all agent actions. This ensures compliance and provides feedback for safely increasing autonomy over time.
+- **Autonomy matching**: Tie autonomy levels to reversibility and blast radius. High-risk operations (deployments, credentials, role assignments, policy changes) always require human approval.
+- **Observability from day one**: Log and audit all agent actions, to ensure compliance and to provide feedback for safely increasing autonomy over time.
 
 ## Next steps
 
-This module established the foundation. The next module in this learning path applies this foundation directly: you'll compare all four Microsoft agentic solution families — GitHub Copilot, Azure Copilot agents, Azure DevOps AI capabilities, and MCP-enabled extensibility — against concrete DevOps task profiles and build a selection framework for your team's environment.
+This module only established the foundation. The next modules in this learning path apply this foundation directly  against concrete DevOps task profiles and build a selection framework for your team's environment.
 
 ## Learn more
 
 - [Microsoft Azure Copilot overview](https://learn.microsoft.com/azure/copilot/overview)
 - [GitHub Copilot documentation](https://docs.github.com/copilot)
 - [Introduction to Azure Copilot agents](https://learn.microsoft.com/training/modules/introduction-azure-copilot-agents/)
 - [Azure DevOps product documentation](https://learn.microsoft.com/azure/devops/?view=azure-devops)
-- [Azure Well-Architected Framework — Operational Excellence](https://learn.microsoft.com/azure/well-architected/operational-excellence/)
+- [Azure Well-Architected Framework—Operational Excellence](https://learn.microsoft.com/azure/well-architected/operational-excellence/)