Dedicated CI resources

Dedicated CI resources assign specific pools of compute capacity to specific types of work, rather than all work competing in a single shared queue. A dedicated runner pool for the platform team's builds ensures their CI times are unaffected by traffic spikes from the product teams. A dedicated pool for AI agent iteration runs ensures agents get immediate runner availability regardless of how busy the main CI queue is. A high-memory pool handles the monorepo builds that need 32GB of RAM, while a smaller instance pool handles the microservice builds that need 4GB.

Resource dedication operates at several levels of granularity. At the coarsest level, different teams get different runner pools, with no sharing between them. At a finer level, different types of workloads within a team get different resources: pre-merge PR checks get fast, expensive runners; nightly full-regression tests get cheaper, slower instances. At the finest level, individual job types are explicitly mapped to runners with specific hardware profiles: high-CPU for compilation, high-memory for test execution, GPU-equipped for ML model builds.

The motivation for dedicated resources is twofold: performance isolation and cost optimization. Performance isolation ensures that a team running a batch of agent iterations doesn't degrade another team's pre-release build performance. Cost optimization ensures that expensive high-performance runners are used for work that actually needs them, while routine checks use cheaper commodity instances. Both motivations become more important as agent usage scales.

For AI-enabled development specifically, dedicated resources solve the "agent burst" problem. Human CI load is relatively smooth and predictable. Agent load is bursty: a developer launching 5 agents simultaneously generates 5x the normal CI load for 10-20 minutes, then nothing until the next iteration batch. Shared queues handle this burst poorly. Dedicated agent pools with auto-scaling capacity handle it correctly: spin up additional runners during burst, scale down during quiet periods.

• Isolation prevents cross-team interference - a team running a large batch of agent CI runs cannot degrade another team's pre-merge validation times when pools are separate
• Sizing is more accurate - a pool dedicated to a specific workload type can be sized precisely for that workload's resource requirements rather than sized for an unpredictable mix
• Cost allocation becomes visible - dedicated pools make it possible to attribute CI costs to teams or workload types, enabling informed decisions about where to invest in build optimization
• Agent-specific pools can be auto-scaled - agent CI load is bursty in a predictable way; auto-scaling dedicated pools for agent runs handle burst more cost-effectively than shared fixed-size pools
• SLOs become enforceable - you can't guarantee a 5-minute CI SLO for human PRs on a shared queue; on a dedicated pool with appropriate capacity, SLOs are achievable and measurable

1. Classify your CI workloads - List every recurring CI workload: human pre-merge PR checks, agent iteration runs, nightly regression tests, release builds, scheduled security scans. For each, note: frequency, duration, resource requirements, and priority (what's the cost of this workload being delayed by 10 minutes?).
2. Create runner pools by priority tier - Start with three pools: high-priority (human pre-merge checks, release builds), medium-priority (agent iteration, team CI), low-priority (nightly jobs, optional checks). Route workloads to pools based on priority. In GitHub Actions, this is runner groups; in GitLab CI, it's runner tags.
3. Configure auto-scaling for agent pools - Agent iteration pools should auto-scale because their load is bursty. Use GitHub Actions Runner Controller (ARC), GitLab's Kubernetes executor, or a cloud auto-scaling group to spin up runners on demand and terminate them when idle. Set a minimum of 0 idle runners (true auto-scale from zero) and a maximum sized for peak burst.
4. Assign appropriate hardware to each pool - High-priority human PR pools: fast CPUs, moderate memory (c5.2xlarge or equivalent). Agent iteration pools: moderate CPUs, high disk I/O (because agents do many file operations), SSD-backed. Nightly full-regression pools: cheapest instances that can run the suite within the night window.
5. Implement cost allocation tagging - Tag all CI runs with team, workload type, and triggering user or agent. Export CI cost data to your cost management system. This makes the cost of agent CI visible and attributable, which is necessary for informed capacity planning.
6. Set SLOs per pool and alert on violations - Define a 95th percentile queue wait time for each pool. For human PR checks: under 2 minutes. For agent iterations: under 5 minutes. Alert when these thresholds are exceeded, and use the alerts to drive capacity adjustments.

Creating too many pools too early. Starting with 10 separate pools before you have the data to size them correctly leads to either over-provisioned idle capacity or under-provisioned pools that don't deliver the intended isolation. Start with 3 pools (high/medium/low priority), collect data for a month, then refine.

Not accounting for burst in pool sizing. A dedicated agent pool sized for average agent CI load will be fully occupied at peak burst, creating queue times that defeat the purpose of separation. Size for the 95th percentile burst load, not the mean. With auto-scaling, this is handled dynamically.

Forgetting about shared dependencies between pools. If multiple pools share a remote build cache or artifact repository, a spike in one pool can cause contention on shared infrastructure even if the compute pools are separate. Ensure shared downstream infrastructure can handle the aggregate load across all pools.

Letting teams bypass pool routing. If developers can submit runs to any pool by changing their CI configuration, the isolation breaks down. Enforce pool routing via org-level CI configuration templates or branch protection rules that define which pipeline runs on which pool.

Not revisiting pool sizing as agent adoption grows. Dedicated pool sizing needs to be reviewed quarterly as agent adoption increases. A pool sized for 5 developers running agents will be inadequate when 30 developers are running agents. Build a capacity review process into your quarterly DevEx review.