What GenOps is
GenOps (Generative Operations) is the operating model for building, deploying, and governing AI assisted software delivery inside real engineering organizations.
It sits at the intersection of:
- Software engineering (how teams build products)
- DevOps (how teams ship and run systems)
- AI enablement (how teams safely use frontier models, agents, and copilots)
- Governance (how teams control cost, risk, data, and access)
GenOps is not “use a chatbot.” It is the practical system for turning AI into repeatable engineering output without breaking security, quality, or budgets.
Why GenOps matters right now
Most teams are in one of these states:
- People are using AI tools ad hoc, with no shared patterns.
- Security and compliance teams are blocking usage because data boundaries are unclear.
- Costs are unpredictable, and quality is inconsistent.
- Early “agents” exist, but they do not survive contact with production realities: identity, logs, approvals, rollback, and audit.
GenOps solves that gap by creating a controlled path from experimentation to production:
- Sandbox environments that match real constraints
- Standard toolchains and workflows
- Secure model access patterns
- Agent lifecycle management
- Observability, cost controls, and governance from day one
What Kimbodo GenOps delivers
Kimbodo GenOps is a fixed scope pilot + POC program that gets an engineering org unblocked, aligned, and shipping AI enabled workflows with confidence.
In a typical engagement, we deliver:
1) Engineering sandboxes and dev environments
- Reproducible sandbox setup for dev and experimentation
- Clear separation of dev, staging, and production patterns
- Baseline identity, secrets management, and access controls
2) Tooling selection and operating standards
- Review current stack and maturity
- Recommend a “least regret” toolchain for GenAI development
- Establish standard workflows: prompt management, evals, CI gating, deployment patterns
3) DevOps + AI DevOps first implementation
- Model access patterns (hosted vs API) with policy controls
- Agent execution patterns (queues, workflows, retries, approvals)
- Release discipline for AI features: versioning, rollback, and safe rollout
4) Co-agent development enablement
- Define where agents belong: support, ops, engineering, or customer workflows
- Permissioned skill design so agents do deterministic actions safely
- Guardrails: tool permissions, data boundaries, and audit logs
5) Frontier application POC
One production realistic POC that proves value, such as:
- Internal support agent for engineering and ops
- Automated incident and triage assistant wired to your systems
- Code change assistant with approvals and safe execution
- Data tagging or document workflow automation
What the pilot costs
Kimbodo GenOps Pilot + POC: $30,000
Designed as a fast, contained engagement that leads naturally into larger delivery if it works.
The objective is simple: by the end of the pilot, you have a working foundation, a real POC, and a clear path to scale.
Who it is for
- VP Engineering, CTO, Head of Platform, Head of DevOps
- Teams who want AI enabled delivery but need guardrails
- Organizations that want speed without chaos
Outcomes you can measure
- A sandboxed environment and repeatable patterns for AI development
- A defined toolchain and operating standards engineers will actually use
- A working POC integrated into real systems
- Clear next steps: scale plan, cost model, and delivery roadmap