feat(roadmap, k8s): add turnkey Grafana dashboards for all Kubernetes stacks (#293) - #294
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…e LightLLM and Portkey AI routing (#292) * feat(ai, k8s): prioritize roadmap into 0.3.x-0.5.x lines and integrate LightLLM and Portkey AI routing * feat(roadmap, k8s): add turnkey Grafana dashboards for all Kubernetes stacks (#293) (#294) * feat(roadmap, k8s): add turnkey Grafana dashboards for all Kubernetes stacks (#293) * docs(sdlc): fix Phase 6 Mermaid release choreography diagram
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* feat(release): v0.2.21 (#279) * fix(reliability): harden exception handling, optimize telemetry & clean data tier (#280) (#281) * feat(reliability): harden exception handling, optimize telemetry & clean data tier (#280) - Harden exception handling across AI, Git, Security, and Core modules with explicit error types and context - Prevent silent error suppression and arbitrary default fallbacks across public APIs - Optimize Jaeger/OTel traces, Prometheus metrics, and FluentBit logging configurations - Enhance GitHub rate limiting client with header tracking and proactive quota safety - Clean up .data directory handling, configure dedicated data.dir path, and deduplicate config writes - Add forward-looking roadmap initiatives for process group management and merge readiness - Expand test suites and architectural invariants for data dir config and stray script prevention - Track deliverable completion in docs/agent/tasks/task-280-harden-exceptions-telemetry-data-tier.md * fix(security): sanitize CodeQL clear-text secret logging in settings and credentials (#280) * feat(ai): multi-scale semantic outline & inspectional scanner (#272) (#282) * feat(ai, telemetry): dynamic slot leasing, telemetry deduplication, and context-aware file review (#283) (#284) - Implement dynamic least-loaded slot leasing across candidate Ollama servers using condition variables to prevent Head-of-Line blocking. - Add multi-layered file classification (shebangs, MIME types, AST/JSON/YAML/TOML parsing, canonical filenames/extensions) to route documentation, configuration, and code files to specialized review task prompts and persona subsets. - Optimize review pre-analysis batching to run static AST extraction without redundant LLM chat calls. - Fix OTelTyper lazy proxy registration to attribute code.namespace and code.function to target commands and eliminate duplicate telemetry attributes. - Add comprehensive test suites for slot leasing, file classification, and telemetry deduplication. * feat(ai): priority classification for AI/LLM requests (#285) (#286) * docs(roadmap): expand v0.2.24 with 8 vibes-grounded improvements from Obs 18-20, Systems 09-10 New roadmap items derived from empirical vibes observations: P0 - Critical: - Lazy Domain-Gated MCP Tool Schema Hydration (Obs 18): 100+ tools consume 25% of context window; lazy hydration reclaims 85% - Pipeline Stage Context Budgeting & Invariant Pinning (Obs 19): sequential pipeline context bloat; invariant eviction under multi-turn drift - Capability-Gated Model Failover & AIMD Batch Recovery (Obs 20): 70B→14B failover cliff; one-way embedding batch ratchet P1 - High: - Lossless Structured Error Reflection for Schema Retries (Obs 19): 256-char truncation forces multi-turn retry loops - Background Shell Pipe Deadlock Fix & Output Contract (Systems Obs 09): 64KB pipe buffer deadlock on verbose commands - Structured Constraint Propagation Across Subagent Delegation (Systems Obs 09): 3-layer delegation retains only 61% fidelity - MCP Resource-First Data Access & Tool Output Sandboxing (Systems Obs 10): Resources 3x cheaper for read-heavy patterns - Lossless Structured Error Reflection (Obs 19): single-turn correction via structured field-path errors * feat(ai): priority classification for AI/LLM requests (#285) * feat(roadmap): add GitHub/VS Code agentic integrations and Grafana dashboards (#287, #289) (#288) * feat(roadmap): add GitHub and VS Code agentic integrations (#287) * feat(roadmap): add devops-cli Grafana dashboards suite (#289) * feat(ai): track approximate lifetime spend per backend service with prometheus and grafana observability (#290) (#291) * feat(ai): track approximate lifetime spend per backend service with prometheus and grafana observability (#290) * fix(security): resolve Bandit B608 by using static parameterized queries in SpendLedger * feat(ai, k8s): prioritize roadmap into 0.3.x-0.5.x lines and integrate LightLLM and Portkey AI routing (#292) * feat(ai, k8s): prioritize roadmap into 0.3.x-0.5.x lines and integrate LightLLM and Portkey AI routing * feat(roadmap, k8s): add turnkey Grafana dashboards for all Kubernetes stacks (#293) (#294) * feat(roadmap, k8s): add turnkey Grafana dashboards for all Kubernetes stacks (#293) * docs(sdlc): fix Phase 6 Mermaid release choreography diagram * feat(roadmap): synchronize milestones, issues #307-#326, release epics, and project fields (#327) * docs(release): compile v0.2.21 changelog notes (#328) * fix(release): query milestone deliverables and enforce branch protection in pre-commit (#331) * fix(release): query milestone deliverables and enforce branch protection in pre-commit (#330) * build(ci): move devops ci quality gate to pre-push hook
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Summary
Closes #293.
Adds turnkey Grafana observability dashboard portfolio specifications and GitOps sidecar provisioning tasks for all operational Kubernetes stacks in
k8s/to the roadmap (docs/ROADMAP.md) under milestonev0.2.23:k8s/monitoring/dashboards/k8s-llm-mesh.json): Token generation throughput (tok/sec), TTFT, prompt processing latency, GPU KV cache memory utilization, tensor parallelism synchronization latency, request concurrency, queue depth, AWQ vs. TokenAttention throughput comparison across Ollama, LiteLLM, Portkey, vLLM, and LightLLM.k8s/monitoring/dashboards/k8s-ai-gateway.json): LiteLLM and Portkey routing metrics, virtual model alias distributions (devops-chat,devops-coder,devops-reasoning,devops-embedding), least-busy routing distribution, circuit breaker trips, failover transitions, Valkey L2 cache hit ratios, and HTTP status codes (200, 429, 502, 503).k8s/monitoring/dashboards/k8s-argocd-gitops.json): Application sync status (Synced,OutOfSync), sync duration histograms, controller reconciliation phase rates, git repository fetch latency and caching, application health states (Healthy,Progressing,Degraded), resource count per app, auto-sync and prune events, ArgoCD API server request latency and error rates.k8s/monitoring/dashboards/k8s-coredns.json): Query latency percentiles (p50/p95/p99), request rates by protocol (UDP/TCP) and query type (A, AAAA, SRV, PTR), response code distributions (NOERROR,NXDOMAIN,SERVFAIL,REFUSED), DNS cache hit/miss ratios, upstream forwarder latency, plugin processing durations.k8s/monitoring/dashboards/k8s-gpu-hardware.json): Real-time GPU compute utilization (%), streaming multiprocessor (SM) occupancy, GPU memory (VRAM) allocated vs. total, GPU temperature and thermal throttling states, power draw (watts) vs. TDP limits, PCIe bus throughput, GPU Feature Discovery (GFD) label allocation and pod-to-GPU binding.k8s/monitoring/dashboards/k8s-loki-logging.json): Ingestion stream rates and bandwidth (MB/sec), chunk compression ratios, memory chunk pool utilization, query evaluation latency and throughput, storage write rates to volume storage, Promtail scrape targets, log line drops, error frequency breakdown by namespace (llm,argocd,monitoring,squid,registry).k8s/monitoring/dashboards/k8s-prometheus-engine.json): Target scraping latency and failure rates, active time-series count, head chunk allocation and TSDB block compaction durations, WAL writes and memory buffers, query execution durations, Alertmanager notification latency, rule group evaluation timing and alerting state tracking.k8s/monitoring/dashboards/k8s-otel-tracing.json): OTel receiver span and metric ingestion rates, pipeline processor queue depth and memory ballast, exporter delivery latencies and HTTP retry rates, dropped span/metric counters, Jaeger/Tempo trace storage throughput, trace-to-metric exemplar links, sampling decision efficiency.k8s/monitoring/dashboards/k8s-registry.json): Image push and pull throughput (MB/sec), image manifest and blob layer request rates, HTTP response codes (200, 201, 404, 500), storage volume capacity utilization, garbage collection runtime and reclaimed storage, catalog size and repository count.k8s/monitoring/dashboards/k8s-squid-egress.json): HTTP and HTTPS CONNECT egress request volume, client connection concurrency, bandwidth consumption (egress/ingress MB/sec), domain access classification (whitelisted vs. blocked), cache hit ratios for model weights and packages, DNS lookup latency via Squid helper, proxy response codes.Automated GitOps ConfigMap Packaging & Synchronization:
devops grafana dashboards sync, packaging each stack dashboard as a labeled Kubernetes ConfigMap ink8s/monitoring/dashboards/for auto-reload by the Grafana sidecar.devops grafana dashboards validate.Realigned Prioritization Matrix:
Turnkey Kubernetes Stacks Grafana Observability Dashboard Suiteunder Major Projects (High Value, High Effort, Scheduled forv0.2.23).Verification
docs/agent/tasks/task-293-k8s-stacks-grafana-dashboards.mdauthored as merged/committed.uv run devops cipassed 100% locally across all 10 quality gates.