If you’ve spent any time in tech, you’re already aware that AI is making big moves. Once upon a time, we relied on AI for mere code generation. Now, we can count on it for day-to-day infrastructure and operations support. For a glimpse into this shifting reality, just take a look at AWS DevOps Agent and DuploCloud’s AI DevOps Engineers.
Each one offers AI-assisted operations, of course, but they approach the problem from different starting points.
They also solve different parts of your daily operational burdens.
Why should you care? Because understanding those differences will help you decide where to let AI take on some of your workload. Or, you may just want to use AI to help you make informed decisions.
Let’s talk about those differences and how you can decide.
The AWS DevOps Agent: When You’re Investigating and Looking for Information
First up, we have the AWS DevOps Agent. This AI system focuses on helping your team understand what’s happening inside AWS environments.
The agent will:
- Connect to AWS services, like CloudWatch, EKS, and CI/CD tools
- Analyze logs, metrics, and resource topology for you
- Provide you with incident summaries and even make recommendations for mitigation
AWS designed its model to fit cleanly into all the existing AWS operating practices. With it, your team can retain full control over remediation. You’ll do this by applying the AI’s recommendations through humans, runbooks, infrastructure as code, or pipelines.
There are plenty of companies that can stop here. After all, the AWS agent will give you more clarity during incidents. It will also standardize any investigations. And you never have to change who makes the actual decisions… or who carries them out.
Duplo AI DevOps Engineers: When You Need an Actual Operator
DuploCloud sits in pretty stark contrast to AWS. These AI DevOps engineers start from a different premise:
Most operational work slows teams down because all that important context and execution live in people’s heads.
DuploCloud created our AI DevOps Engineers to operate inside a platform.
From within, the platform already defines:
- The application
- The environment (dev, staging, prod)
- The security boundary
- Ownership and blast radius
In this situation, you have context that already exists upfront. This means that Duplo’s agents behave more like onboarded DevOps engineers than analysis tools.
They can investigate issues, propose changes, and carry out workflows with multiple steps. All they need is explicit human approval.
The goal with DuploCloud is not just better explanations. It’s less manual DevOps work.
So… What’s the Difference, In Practice?
When you’re actually on the ground, dealing with both systems, here’s where you’ll see points of divergence:
1. Assembly vs Ready-to-Run
With the AWS DevOps Agent, meaningful outcomes usually mean teams have to assemble and maintain:
- IAM Roles and Agent Spaces
- Permissions and access boundaries
- Integrations with observability, CI/CD, and ticketing
- Human workflows to apply fixes
The good news is that the agent will coordinate these components. The bad news is that it won’t replace the work of building and maintaining them.
In contrast, DuploCloud provides you with agents that are pre-integrated with application context and guardrails. So your teams don’t need to wire together tooling. Nor do they need to define how resources map to applications before they start using the agents.
The primary difference lies in time and effort.
2. Putting Things in Context: Application vs Resource
The AWS DevOps Agent operates at the resource level. This means it sees EC2 instances, EKS clusters, IAM roles, logs, and metrics. From there, it will infer application behavior from those signals.
On the flipside, our Duplo AI DevOps Engineers operate at the application level. This means that a Duplo Tenant will explicitly define what belongs together. These could range from services, infrastructure, and environments to entire teams. So the agents don’t have to guess whether something is safe to change.
Here, you can clearly see why AWS focuses on recommendations while Duplo aims at execution with approval.
3. Investigation vs Execution: What It Really Means
The bottom line is this,
AWS DevOps Agent optimizes for:
- Investigation
- Explanation
- Recommendation
Meanwhile,
Duplo AI DevOps Engineers optimize for:
- Investigation
- Execution with guardrails
- Validation and learning
So, while you’ll get an investigation in both cases, where the investigation goes is wildly different.
In practice, each one approaches how work flows through a team in distinct ways.
In the Duplo user journey, onboarding an AI DevOps Engineer takes minutes instead of days. The agent can run discovery, generate topology and documentation, and institutionalize that knowledge immediately.
With Duplo, our agent will collaborate with engineers in real time during any incident. Automation will store the full debugging context, and it will apply fixes with review.
And you’ll never have to worry about it hoarding critical details as tribal knowledge.
4. Where You’ll Actually Save Time
From an engineering leader’s perspective, the most meaningful difference is the one that shows up in routine work.
This includes:
- Incident triage and root cause analysis
- Environment setup and service provisioning
- Safe changes in non-production and production systems
In traditional workflows, these tasks can take hours or even days across investigation, coordination, and execution. Duplo’s model shrinks that cycle way down. Our biggest success is that we start with context. So the agent isn’t starting from zero with every incident.
From there, it can carry tasks through to completion.
And of course, humans stay in complete control.
5. Scope and Portability
In terms of scope, AWS DevOps Agent operates solely within AWS.
Duplo’s AI DevOps Engineers, however, operate consistently across AWS, Azure, and GCP. And they can use the same application model and workflows throughout.
If your team is managing multiple clouds or planning for portability, you’ll never need to rebuild operational practices with each provider.
So… Which Approach Is Right for You?
Look, some teams justwant better insight into incidents. And that’s okay. They prefer to keep any remediation fully manual. The AWS DevOps agent can be a perfect fit in that case.
But other teams do want to cut their operational load by letting AI handle the routine, tedious tasks for them. Of course, they want to be sure those tasks are handled safely and with guardrails in place. In that case, Duplo’s AI DevOps Engineers are outstanding helpers.
And in the end, these approaches are not mutually exclusive. They simply reflect different philosophies about where AI should stop and where it should act.
That’s up to you and your team.
See the Difference in Practice
The fastest way to understand the difference between investigation-first and execution-capable agents is to get right into the mix.
You can check out the DuploCloud sandbox today. There, you can interact with AI DevOps Engineers, walk through real workflows, and see how application context and approval-based execution can change your day-to-day operations.
👉 Try the Duplo sandbox to explore how application-aware AI DevOps Engineers work in practice.
