The Hidden Half of Model Management: A 4-Phase Deprecation Framework for Multi-Agent Systems Using LUMOS

The Hidden Half of Model Management: Deprecation in Multi-Agent Systems Enterprise AI teams often pour energy into onboarding the latest models—comparing benchmarks, tuning prompts, and setting up inference pipelines. Yet the structured retirement of older models remains an afterthought, handled reactively when a vendor sunsets an API or costs quietly balloon. In multi-agent systems, this imbalance is dangerous. Each agent may depend on a specific model version, and an abrupt removal can cascade into discovery failures, degraded user experiences, or compliance gaps. This article introduces a four-phase deprecation framework tailored to multi-agent architectures running on the LUMOS platform. LUMOS provides multi-agent orchestration, RAG integration, and cross-model observability, making it uniquely suited to manage model transitions safely. The framework covers dependency mapping, perfor

mance baselines, staggered migration with fallback, and post-retirement monitoring. Practical checklists and rollback planning help you adopt this approach immediately with your current teams. Phase 1: Dependency Mapping for Model-to-Agent Links Before any migration, you must know which agents depend on which model instances. In LUMOS, each agent declaration includes a string (e.g., or ). Start by auditing all active agents: - Inventory agents and their associated model IDs. Export the agent list from LUMOS’s control plane or query the agent metadata API. Note any non-trivial dependencies such as function-calling settings, token limits, or safety configurations that differ between model versions. - Identify shared model instances. A single model deployment might serve multiple agents. Retiring that model affects every downstream agent. - Document external integrations. If agents call ext

ernal APIs (e.g., search, database), those interfaces may rely on specific response formats tied to the old model. Checklist for Phase 1: - [ ] Full agent inventory with current . - [ ] List of shared model deployments. - [ ] Map of external integrations per agent. - [ ] Categorize model versions by vendor, tier, and retirement risk (vendor-announced sunset, internal performance decay). Phase 2: Performance Baseline Comparison A deprecation decision must be grounded in data, not impulse. Compare the old model against its replacement across the metrics that matter for each agent’s task: - Accuracy & completion rate – Use historical traces from LUMOS’s built-in evaluation runs. Run a side-by-side comparison on a held-back test set that mirrors production traffic. - Latency & cost – Record p50/p99 response times and per-token costs (as available from your LUMOS billing dashboard). Remember

that image or video inputs can multiply token counts per vendor pricing; note the exact strings to avoid mixing tier definitions. - Safety & alignment – Review red-teaming outputs if your domain requires consistency (e.g., healthcare, financial advice). Many vendors update safety filters with new model versions. Pro tip: LUMOS’s experiment management feature lets you route a percentage of live traffic to the candidate model while shadowing the old one. This eliminates guesswork and surfaces real-world differences in behavior. Checklist for Phase 2: - [ ] Define a metric suite (accuracy, latency, cost, safety flags). - [ ] Run A/B or shadow tests for at least three business cycles. - [ ] Document any regressions or improvements per agent. - [ ] Decide go/no-go for each model-to-agent transition. Phase 3: Staggered Migration with Fallback Never switch every agent at once. Use a phased roll

out: 1. Pilot group – Select two or three low-risk, low-traffic agents. Update their to the new version. Monitor for 24-48 hours. 2. Incremental expansion – Double the scope every other day, moving to medium-traffic agents. Keep old model instances warm during this period (cost permitting). 3. Cutover – When confidence is high, update the remaining agents. Retain the old model deployment (or a cached fallback endpoint) for at least a full training cycle (e.g., two weeks). Rollback plan essentials: - Every agent migration must be reversible: store the previous in a configmap or LUMOS environment variable. - Use canary deployments where possible. LUMOS supports traffic splitting by agent ID or user segment; reconfigure the split to 100% old model if anomalies appear. - Document the exact command or UI steps to revert. Test the rollback in a staging environment before production. Checklist

for Phase 3: - [ ] Define pilot agent list. - [ ] Set up canary or shadow routing in LUMOS. - [ ] Document rollback procedures and share with on-call team. - [ ] Keep old model endpoint active during migration window. - [ ] Monitor agent success rates and alert on drift 5% from baseline. Phase 4: Po