DevOps Agent Benchmark
20 Infrastructure Tasks. 3 Very Different Architectures.
Every benchmark says "AI can code." Infrastructure teams need a different question: "Can it reason through real platform operations under guardrails?" We tested that directly.
Last verified against vendor docs and release notes on February 27, 2026.
The Benchmark Gap Is Real
Most public leaderboards prioritize software issue resolution and coding tasks. That data is useful, but it under-represents the day-to-day work of platform teams: Terraform module discipline, multi-step incident triage, pipeline migration, and policy-aware infrastructure changes.
Zara verdict (Lab mode): if your team ships infrastructure, evaluate AI on infrastructure behavior, not autocomplete aesthetics.
Terraform module generation
Generate modules from standards, wire variables/outputs, and keep plans valid without manual patching.
Infrastructure composition
Translate intent like "2 web apps + 1 key vault" into dependency-aware IaC scaffolding.
Kubernetes incident triage
Diagnose CrashLoopBackOff paths from logs, probes, resource limits, and config drift.
CI/CD workflow engineering
Create and migrate pipelines (including Jenkins-to-Actions patterns) with security and approval gates.
Security and reliability review
Review Helm and pipeline configurations for blast radius, secrets handling, and policy guardrails.
Precision Fixes Before You Decide
Two claims in the market narrative needed correction during fact-checking:
GitHub Agentic Workflows: currently documented by GitHub Next as a research demonstrator, not a general product launch.
Amazon Q vs AgentCore: Cedar policy conversion and 13 built-in evaluators are AgentCore capabilities. Useful, but different from core Q Developer claims.
What 20 Tasks Revealed
GitHub Copilot is IDE-centered with workflow guardrails
GitHub coding agent runs asynchronously in a GitHub Actions-backed environment with explicit handoff to pull requests.
GitHub docs state coding agent workflow runs are not executed automatically and require approval in Actions.
Inference: this architecture is strong for governed repo workflows, but infra reasoning quality still depends on the context you provide and validation gates you enforce.
Claude Code is terminal-native and tool-centric
Anthropic documents Claude Code as a command-line tool that can execute commands and use tools under user-defined permissions.
Claude Code slash commands include `/mcp`, making MCP server integration first-class in terminal workflows.
Inference: multi-step incident/debug tasks were easier to keep coherent when the agent stayed in terminal context across commands.
Amazon Q Developer is deeply AWS-integrated
AWS documents MCP support for Amazon Q Developer in CLI and IDE contexts, including local stdio plus remote HTTP/OAuth servers.
AWS also added Q Developer operational investigation support in CloudWatch (preview), reinforcing AWS-native incident workflows.
Important correction: Cedar policy translation and 13 built-in evaluators are AgentCore capabilities, not core Amazon Q Developer features.
Build Your 5-Task Reality Check
15 Minutes Well Spent
Run five tasks from your own environment before picking a platform. Vendor demos optimize for vendor strengths. Your incident history exposes real capability.
Use one Terraform task from your real module standard.
Use one natural-language infra intent conversion task.
Use one real K8s incident from last month.
Use one pipeline migration from your current stack.
Use one security review task with your policy expectations.
Starter Harness
# 5-task benchmark harness (copy and adapt)
## Task 1 - Terraform module from your standards
- Input: your naming policy, required tags, provider version floor
- Output checks:
- terraform validate passes
- no hardcoded environment values
- required variables and outputs present
## Task 2 - Intent-to-IaC composition
- Prompt example: "I need 2 web apps, 1 key vault, private endpoints, and staging/prod separation"
- Output checks:
- dependency graph is correct
- env boundaries are explicit
- state layout is reviewable
## Task 3 - K8s incident triage
- Input: real log excerpt + deployment/service manifests
- Output checks:
- agent proposes ordered triage steps
- references concrete signals (probe failures, OOM, config mismatch)
- includes safe rollback path
## Task 4 - Pipeline migration
- Input: one real Jenkins pipeline + target GitHub Actions policy
- Output checks:
- preserves stages and approvals
- secrets are handled with platform-native controls
- rollback path exists
## Task 5 - Security review
- Input: Helm chart + CI workflow
- Output checks:
- catches privilege escalation and secret exposure risks
- proposes practical remediations
- avoids destructive changes without approvalSources & Verification
Verified on February 27, 2026. Where this article makes conclusions across tools, those are explicitly marked as inference from documented product behavior plus benchmark observations.
Want the Build Pattern?
I built a companion TNTM playbook that shows exactly how to combine Skills, Agents, and MCP to generate Terraform modules from standards and compose IaC from user intent.
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