Converxys Tech Blog

Classic Horizontal Pod Autoscaling (HPA) reaches its limits with inference-heavy workloads. Cold-start penalties, GPU binding, and heterogeneous load profiles require additional levers. A resilient design blends HPA, Vertical Pod Autoscaler (VPA), and Cluster Autoscaler within a policy engine fed by Prometheus metrics and OpenTelemetry traces.

Key building blocks of such a setup include:

• Signal Aggregation Layer: Vectorizing metrics like queue_latency_p95, token_throughput, and gpu_memory_pressure to forecast the required pod footprint.
• Predictive Pre-Warming: Time-series models (e.g., ARIMA) that stage GPU pods ahead of surge windows to mitigate cold starts.
• Slice-based Scaling: Splitting the inference pipeline into tokenizer, embedding service, and decoder with dedicated HPAs and QoS classes.

if gpu_memory_pressure > 0.72 and queue_latency_p95 > 250ms:
    target_replicas = ceil(current_rps / desired_rps_per_pod)
    cluster_autoscaler.scale(node_pool="gpu-a100", count=target_replicas / pods_per_node)
else if token_throughput < target and gpu_utilization < 0.4:
    vpa.recommend(memory="32Gi", cpu="6")

Regular chaos engineering in canary namespaces further stresses the system. Fault-injection scenarios uncover weak network paths or GPU workload isolation issues and provide data points for tighter SLOs.

Enterprise AI agents need robust guardrails. Operator graphs with four layers—signal acquisition, cognitive core, tool abstraction, and action commit—have proven effective. Each layer can be versioned, rolled out via canaries, and validated using Open Policy Agent (OPA).

• Adaptive Retrieval: Combining dense and sparse vector spaces (Faiss & BM25) plus runtime query classification. Frameworks like the LangChain Expression Language (LCEL) enable dynamic retrieval strategies.
• Stateful Reasoning: Persisting agent state in Redis Streams with event sourcing to make deterministic replays and audits possible.
• Human-in-the-Loop: Slack or Teams workflows that open an approval gate when confidence scores fall below 0.78 or policy constraints are triggered.

Tool contracts safeguard unstable or slow APIs. The OPA rule below shows how external write operations can be tied to an audit token:

package agents.guardrails

default allow = false

allow {
    input.tool == "jira.create_issue"
    input.context.audit_token_verified
    input.payload.criticality <= 2
}

Patterns like these typically bring the mean time to detect undesired behavior down to a few minutes and keep large agent fleets stable.

With increasing platform complexity, traditional three-pillar monitoring falls short. Modern setups introduce a telemetry mesh consisting of OpenTelemetry Collector, Tempo, Loki, and Grafana, augmented by explainability services.

• Prompt and Token Tracing: Treat each prompt as a span, capturing token cost, response time, and embedding version to surface regressions shortly after model updates.
• Concept Drift Detection: Perform online scoring against reference distributions with libraries such as River. Alerts fire when thresholds like Jensen-Shannon divergence exceed predefined limits.
• SLO Backpropagation: Cascade error budgets through the service catalog down to individual prompt routes so teams can prioritize mitigations.

The following OpenTelemetry attribute set illustrates useful metadata beyond standard metrics:

{
  "llm.provider": "openai",
  "llm.model": "gpt-4.1-mini",
  "prompt.route": "contract-review:v2",
  "retrieval.latency_ms": 83,
  "guardrail.policy_version": "2025-08-14",
  "slo.burn_rate": 0.42
}

Correlated dashboards help surface "shadow failures"—issues temporarily masked by manual workarounds—while automation pipelines can replay regression tests against the last known good prompt version.

Many organizations adopt managed LLM APIs to automate service desks. Tickets flow through an event-bus pattern, are classified by a lightweight agent, and receive relevant knowledge base articles. Fine-tuning is rarely required; prompt templates inject metadata from systems like ServiceNow or Jira Service Management.

• Context Collector: GraphQL resolvers consolidate CMDB records, FAQ documents, and SLA definitions.
• Decision Router: Workflow engines such as Temporal set priority, urgency, and ownership.
• Human Handover: Adaptive cards in Microsoft Teams or Slack conveniently hand off critical cases to L2 teams.

These architectures shorten first response times and, through feedback loops, build a continuously updated knowledge base that increases the automation rate over time.

Retrieval-augmented generation (RAG) is well-suited when domain knowledge must be exposed without training custom models. Documents can be indexed by tenant, validity, and security tier. Dual-vector search (OpenAI embeddings plus BM25) blends semantic and keyword-based matches.

• Ingestion Pipeline: Delta Lake combined with EventBridge makes new documents searchable within minutes.
• Response Formatter: JSON-schema outputs let CRM or portal frontends render structured answers.
• Compliance Layer: Data-loss prevention rules mask sensitive content before it reaches prompts.

The outcome is a knowledge bot capable of safely serving product roadmaps, contract clauses, or price lists—auditable and fine-tuning free.

Generative APIs can accelerate ERP workflows without operating custom models. Incoming quote or order requests are normalized through OCR and named entity recognition, then routed through a rules and orchestration platform such as Camunda 8.

• Input Normalization: Pre-built cloud services extract relevant entities and structure documents.
• Business Rule Engine: DMN tables encode pricing thresholds, discounts, and compliance checks.
• Audit Trail: Decisions with confidence scores are persisted in the ERP change log for transparent reviews.

Approval cycles shorten considerably while governance requirements remain intact. Versioned prompts and policies keep the LLM provider interchangeable.