AI assistants have transformed how developers work. And now coupled with the Cortex MCP that connects AI assistants directly to live service data, ownership records, and organizational standards, developers can get accurate, context-rich answers about their services and standards right in their IDE.
→ Tips and prompts for developers using the Cortex MCP
But what about engineering leaders?! Your opportunities with AI assistants extend far beyond code generation. You need to ensure readiness for major launches, uncover inefficiencies that unlock real savings, and proactively reduce risk across your organization. These types of strategic wins become possible when your AI assistant has access to the right organizational context.
The Cortex MCP is high impact for devs AND engineering leaders. Instead of opening up dashboards or asking someone on your team to hunt down an answer about a particular service, you just prompt your AI assistant.
Here are some practical prompts engineering leaders can start using today.
1. Ensure and Accelerate Production Readiness
Prompt: “Which services aren’t ready for production?”
How it helps with ensuring readiness:
As an engineering leader, you're ultimately responsible for ensuring that everything that ships is reliable. The challenge is that readiness indicators often live across dozens of tools: runbooks in Confluence, on-call rotations in PagerDuty, SLOs in Datadog, security scans in Snyk. Which gaps actually block safe delivery? Which services put customer trust at risk?
Production Readiness Scorecards in Cortex help you and your team understand the health and readiness of your services at a glance.
→ Not using Cortex yet? Get the Production Readiness Scorecard Template in your own Cortex sandbox
The Cortex MCP, right in your AI assistant, provides an answer to “are we ready?” in real time, with full context about organization standards and expectations.
Sample response:
Behind the Scenes:
The Cortex MCP pulled production readiness scorecards across all services, which already evaluated services against defined standards like on-call setup, documentation, SLOs, and security scans. Then, the AI assistant helps cross-reference failures with active initiatives in Cortex, and prioritizes gaps based on however you’ve defined business impact, or infers based on all of the context available.
If prompted, the LLM will give suggested actions, and can even make suggestions for additions to your scorecard or priority next steps. Like the suggested actions? Create a time-bound initiative in Cortex to ensure immediate action is routed to the right team.
The Cortex MCP turns what would have taken a few manual steps—like logging into Cortex to dive into the Production Readiness Scorecard and analyze scores, then thinking about your gameplan and next steps—into an immediate, prioritized action plan.
2. Monitor and Reduce Cloud Costs
Prompt: “Which workspaces are burning unnecessary cloud spend?”
How it helps reduce cloud costs:
Cloud cost overruns quietly drain budgets. A single misconfigured job, underutilized workspace, or unoptimized service can burn hundreds of thousands of dollars annually. Whether it's Databricks compute capacity, AWS EC2 instances, Snowflake warehouses, or GCP BigQuery slots, leaders typically rely on finance-driven reports or scattered dashboards that don't tie spend back to owners or show actionable next steps.
The Cortex MCP changes that dynamic by surfacing problems, attributing ownership, and recommending actions — all in one conversation.
Sample response:
Behind the scenes:
AI assistants use Cortex Scorecards, initiatives, or custom data to analyze costing data.
Send your costing data to Cortex, like from AWS Cost Explorer, Datadog infrastructure monitoring, or Azure Cost Management. Cortex supports third-party integrations, internally hosted integrations, and custom webhook integrations.
Or, kick off an initiative to begin adding custom metrics to track things like:
Monthly cloud spend per service
CPU/memory utilization rates
Cost per request/transaction
Resource allocation vs. actual usage
You can even use the Cortex MCP to quickly check your progress against your cost-tagging goals:
Once you have custom cost metrics, cost-tagging in place, or any active cost-related initiatives or scorecards, use the Cortex MCP to quickly assess status and spot inefficiencies. You can build a scorecard that evaluates services on:
Cost efficiency ratios
Resource utilization thresholds
Rightsizing opportunities
Unused or underutilized resources
You can also use the MCP to identify potential cost waste indicators like:
Low deployment frequency (from DORA metrics) might indicate unused services
Poor code quality scores could correlate with inefficient resource usage
Services without active maintenance might be candidates for decommissioning
Once you spot the inefficiencies and identify areas of improvement using the Cortex MCP, create time-bound initiatives in Cortex to notify the appropriate owners that action is needed.
What would normally require custom dashboards, FinOps reports, and manual tracking and chasing gets surfaced instantly in one conversation.
3. Monitor and Reduce Risk
Prompt: “What services are putting us at security risk?”
Why it matters:
Every engineering leader knows security is a moving target. Patches slip, scanning coverage drifts, and compliance requirements evolve faster than teams can keep up. Identifying vulnerabilities is usually straightforward. But often the real challenge lies in knowing which ones actually put the business at risk, and making sure someone is accountable for fixing them. The Cortex MCP gives you that answer in plain language, with remediation already in motion.
Sample response:
Behind the scenes:
The Cortex MCP and your AI assistant cross-checked service maturity data against security scorecards, identified non-compliant services, and prioritized them by external exposure and business impact. Your AI assistant, now with the Cortex MCP, identifies risk and proposes next steps that you can easily translate into a new initiative or Scorecard. Or, ask your AI assistant who owns your worst-offending services so you can shoot them a Slack DM ASAP 🔥
Your developer portal is just a prompt away
With the Cortex MCP within AI assistants, engineering leaders get insight in performance, prioritization, and next steps. Instead of juggling dashboards, reports, and manual check-ins, they can ask one question and get a trusted, actionable answer.
Learn more about Cortex MCP and start prompting your organization directly.
