Agent Evaluation and Testing#

You cannot improve what you cannot measure. Agent evaluation is harder than traditional software testing because agents are non-deterministic, their behavior depends on prompt wording, and the same input can produce multiple valid outputs. But “it is hard” is not an excuse for not doing it. This article provides a step-by-step framework for building an agent evaluation pipeline that catches regressions, compares configurations, and quantifies real-world performance.

Database Testing Strategies#

Database tests are the tests most teams get wrong. They either skip them entirely (testing with mocks, then discovering schema mismatches in production), or they build a fragile suite sharing a single database where tests interfere with each other. The right approach depends on what you are testing and what tradeoffs you can accept.

Fixtures vs Factories#

Fixtures#

Fixtures are static SQL files loaded before a test suite runs:

Docker Compose Validation Stacks#

Docker Compose validates multi-service architectures without Kubernetes overhead. It answers the question: do these services actually work together? Containers start, connect, and communicate – or they fail, giving you fast feedback before you push to a cluster.

This article provides complete Compose stacks for four common validation scenarios. Each includes the full docker-compose.yml, health check scripts, and teardown procedures. The pattern for using them is always the same: clone the template, customize for your services, bring it up, validate, capture results, bring it down.

kind Validation Templates#

kind (Kubernetes IN Docker) runs Kubernetes clusters using Docker containers as nodes. It was designed for testing Kubernetes itself, which makes it an excellent tool for validating infrastructure changes. It starts fast, uses fewer resources than minikube, and is disposable by design.

This article provides copy-paste cluster configurations and complete lifecycle scripts for common validation scenarios.

Cluster Configuration Templates#

Basic Single-Node#

The simplest configuration. One container acts as both control plane and worker. Sufficient for validating that deployments, services, ConfigMaps, and Secrets work correctly.

Sandbox to Production#

An agent that produces infrastructure deliverables works in a sandbox. It does not touch production. It does not reach into someone else’s cluster, database, or cloud account. It works in an isolated environment, tests its work, captures the results, and hands the human a verified deliverable they can execute on their own infrastructure.

This is not a limitation – it is a design choice. The output is always a deliverable, never a direct action on someone else’s systems. This boundary is what makes the approach safe enough for production infrastructure work and trustworthy enough for enterprise change management.

Template Contribution Guide#

Agent Zone validation templates are reusable infrastructure configurations that agents and developers use to validate changes. A Kubernetes devcontainer template, an ephemeral EKS cluster module, a static validation pipeline script – each follows a standard format so that any agent or developer can pick one up, understand its purpose, and use it without reading through implementation details.

This guide defines the standards for contributing templates. It covers directory structure, required files, testing, quality expectations, versioning, and the submission process.

Testing Infrastructure Code#

Infrastructure code has a unique testing challenge: the thing you are testing is expensive to instantiate. You cannot spin up a VPC, an RDS instance, and an EKS cluster for every pull request and tear it down 5 minutes later without significant cost and time. But you also cannot ship untested infrastructure changes to production without risk.

The solution is the same as in software engineering: a testing pyramid. Fast, cheap tests at the bottom catch most errors. Slower, expensive tests at the top catch the rest. The key is knowing what to test at which level.

Testing Strategies in CI Pipelines#

Every CI pipeline faces the same tension: run enough tests to catch bugs before they merge, but not so many that developers wait twenty minutes for feedback on a one-line change. The answer is not running everything everywhere. It is running the right tests at the right time.

The Three Test Tiers#

Tests divide into three tiers based on execution speed, failure signal quality, and infrastructure requirements.

Testing Strategy Selection#

Choosing the right mix of tests determines whether your test suite catches real bugs or just consumes CI minutes. There is no single correct answer – the right strategy depends on your system architecture, team size, deployment cadence, and the cost of production failures.

The Testing Pyramid#

The classic testing pyramid, introduced by Mike Cohn, prescribes many unit tests at the base, fewer integration tests in the middle, and a small number of end-to-end tests at the top.

Validation Path Selection#

Not every infrastructure change needs a full Kubernetes cluster to validate. Some changes can be verified with a linter in under a second. Others genuinely need a multi-node cluster with ingress, persistent volumes, and network policies. The cost of choosing wrong is real in both directions: too little validation lets broken configs reach production, while too much wastes minutes or hours on environments you did not need.