Agentic DevOps · 4 min read
What is agentic DevOps? A practical guide for product teams
What agents can coordinate, what the platform must enforce, and where accountable people fit.
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Practical thinking for the infrastructure behind what your agents build. From the first environment to the next production release.
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Shipping AI apps · 4 min read
How to deploy an AI-built app to production
Reliability · 4 min read
Production readiness checklist for startups and AI-built apps
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20 guides
What is agentic DevOps? A practical guide for product teams
Understand agentic DevOps, where agents fit into cloud operations, and how to introduce automation with clear permissions, checks and ownership.
Definition and adoption guideRead guide
MCP for cloud provisioning: design a safe request-to-resource workflow
A practical design for provisioning cloud infrastructure through MCP, covering typed inputs, plans, permissions, retries and verification.
Architecture and implementation guideRead guide
MCP vs REST APIs for infrastructure automation: which should you use?
Compare MCP and REST for agent-accessible infrastructure, including discovery, authorization, asynchronous operations and shared backend controls.
Interface comparisonRead guide
How to evaluate an agentic DevOps platform: a buyer's checklist
Evaluate agentic DevOps platforms with practical tests for provisioning, deployment, permissions, audit evidence, cost controls and human support.
Platform evaluation checklistRead guide
How to deploy an AI-built app to production
Take an AI-built app from a working prototype to a repeatable production deployment with persistent data, secrets, checks, monitoring and recovery.
Deployment guideRead guide
CI/CD for AI-generated code: build a release process you can trust
Design CI/CD for AI-generated changes with independent acceptance tests, immutable artifacts, scoped deployment identities and recovery checks.
Delivery workflow guideRead guide
Preview environments for AI coding agents: isolation, data and cleanup
Design useful preview environments for AI coding agents with isolated credentials, realistic test data, visible limits and a reliable cleanup lifecycle.
Environment design guideRead guide
Background jobs for AI apps: queues, retries and idempotency
Design background processing for AI apps with durable job state, bounded retries, idempotent side effects, queue monitoring and clear user feedback.
Application architecture guideRead guide
Human approval for AI infrastructure changes: what should be reviewed?
Decide which agent-driven infrastructure changes need human approval and design review around exact plans, impact, expiry and execution evidence.
Change governance guideRead guide
Least privilege for AI agents that manage cloud infrastructure
Scope cloud access for AI agents by task, tenant, environment and duration, with separate execution identities and practical permission tests.
Cloud identity design guideRead guide
Secrets management for AI coding and deployment workflows
Keep credentials out of AI prompts, repositories, browser bundles and tool output with secret references, environment separation and a tested rotation process.
Credential handling guideRead guide
Prompt injection in DevOps tools: protect the execution boundary
Understand how untrusted logs, tickets and repository content can influence DevOps agents, and enforce permissions outside the model's instructions.
Threat model and controls guideRead guide
Production readiness checklist for startups and AI-built apps
A practical production readiness checklist across releases, reliability, security, cloud cost and ownership, with evidence to collect before launch.
Launch readiness checklistRead guide
Observability for AI applications: monitor the whole customer task
Monitor AI applications beyond uptime with request traces, job completion, model dependency signals, cost attribution and privacy-aware telemetry.
Monitoring design guideRead guide
Rollback vs restore: two recovery plans every production app needs
Understand deployment rollback and data restore, where each helps, and how to test recovery with schema changes, recovery objectives and validation.
Recovery comparison and drill guideRead guide
SLOs and error budgets for startups: start with one customer journey
Define a useful first service-level objective and error budget with a clear denominator, measurement window, alert owner and release decision policy.
Reliability planning guideRead guide
Cloud cost controls for AI agents: budgets, limits and unit economics
Control AI application costs with resource ownership, bounded concurrency, model usage limits and cost per successful task rather than budget alerts alone.
Cloud cost management guideRead guide
Ephemeral environment cleanup: stop paying for forgotten previews
Build a cleanup process for temporary cloud environments with owners, expiry metadata, dependency-aware deletion and inventory reconciliation.
Resource lifecycle guideRead guide
Infrastructure as code for AI agents: plans, state and drift
Use infrastructure as code with AI agents through versioned modules, reviewed plans, protected state and explicit handling of drift and concurrent changes.
Infrastructure workflow guideRead guide
Managed DevOps vs in-house: how startups can compare operating models
Compare managed DevOps, in-house ownership and hybrid support using responsibility, coverage, engineering capacity, cloud cost and exit requirements.
Operating model comparisonRead guide
Can your agents
work with your cloud?
Ten questions about infrastructure context, agent permissions, policies, resource ownership and verification. Get a score without granting cloud access.
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