patterns by default:
Deterministic workflow and state-machine architectures for repeatable, high-confidence operational processes.
Planner-executor architectures for decomposing complex investigation, planning, and remediation tasks.
Supervisor-worker or manager-worker architectures for coordinating specialist domain agents.
Router architectures for selecting the right model, tool, knowledge source, workflow, or specialist agent.
Reflection, critic, verifier, or judge patterns for quality assurance, validation, and safety checks.
Event-driven architectures for responding to telemetry anomalies, workload failures, scheduler events, benchmark regressions, and operational alerts.
Human-in-the-loop architectures for high-impact recommendations, privileged actions, or changes to production environments.
Build specialist agents for relevant Model-to-Grid and AI-factory domains, such as:
Benchmark-analysis and performance-diagnosis agents.
Workload recipe and runtime-configuration recommendation agents.
Inference-endpoint selection, capacity, and optimization agents.
Kubernetes and job-scheduler diagnostic agents.
GPU-topology, network, RDMA, storage, and utilization-analysis agents.
AI-factory health, capacity, maintenance, and operational-triage agents.
Documentation, knowledge, incident-review, and runbook-execution assistants.
Thermal domain specific monitoring and optimization agents.
Power domain specific monitoring and optimization agents.
Grid-integration specific monitoring and optimization agents.
Develop the intelligent coordination layer for Model-to-Grid, enabling agents to reason across model characteristics, inference and training configuration, validated recipes, GPU resources, topology, scheduling policies, network and storage performance, capacity, power, thermal conditions, health signals, and operational constraints.
Build Retrieval-Augmented Generation (RAG) pipelines using a combination of vector retrieval, hybrid search, metadata filtering, reranking, structured-data queries, graphbased retrieval where valuable, source attribution, and permission-aware access controls.
Use tools such as pgvector, OpenSearch, Elasticsearch, Milvus, Weaviate, Pinecone, Qdrant, Neo4j, or equivalent data and retrieval platforms as appropriate to the product architecture and deployment environment.
Design knowledge-ingestion pipelines for documentation, runbooks, ticketing systems, configuration repositories, benchmark reports, experiment records, cluster state, telemetry catalogues, incident reports, and approved internal knowledge sources.
Build data and context pipelines that combine unstructured knowledge with structured operational data, including metrics, logs, traces, events, time-series databases, scheduler queues, job states, resource inventories, and configuration-management data.
Integrate agents with governed tools and APIs, including Kubernetes, proprietary scheduler services, observability platforms, benchmark services, inference endpoints, configuration repositories, CI/CD pipelines, ticketing systems, workflow engines,
databases, and operational tooling.
Define tool contracts using structured input and output schemas, typed interfaces, validation, retries, idempotency controls, rate limits, timeouts, circuit breakers, approval requirements, and detailed audit logging.
Build robust agent harnesses that provide context assembly, model routing, prompt and policy versioning, structured output handling, memory management, state persistence, retries, failure handling, task recovery, escalation, and end-to-end tracing.
Implement short-term task memory, long-term user or operational memory where permitted, episodic memory for prior investigations or incidents, and semantic memory based on approved knowledge stores; apply retention, access-control, and datagovernance requirements to each.
Implement model-routing and fallback strategies across self-hosted inference endpoints and approved external models, selecting models according to task complexity, latency, cost, context-window requirement, tool-use capability, privacy needs, and reliability targets.
Partner with the Self-Hosted Inference Platform & Optimization team to ensure that agentic applications have suitable endpoint profiles for planning, reasoning, embeddings, reranking, summarization, classification, tool use, multimodal analysis, and high-throughput operational workflows.
Build agent workflows for detection, diagnosis, recommendation, planning, action simulation, controlled execution, verification, and learning loops.
Develop offline replay, simulation, shadow-mode, and what-if evaluation capabilities to validate recommendations before allowing actions in production—especially for scheduler policies, workload placement, runtime changes, capacity decisions, and operational remediation.
Design human approval and policy enforcement workflows that clearly present an agent’s evidence, recommendation, expected impact, proposed action, confidence, risk classification, authorization scope, and rollback option.
Work with the Security Engineer to implement defense-in-depth controls against direct and indirect prompt injection, insecure output handling, excessive agency, unsafe tool use, data leakage, cross-tenant exposure, privilege escalation, credential misuse, unauthorized actions, and insufficient auditability.
Use policy engines, guardrail frameworks, structured output validation, content and tool filters, permission checks, sandboxing, and allowlisted action patterns to ensure that agent behavior remains bounded and trustworthy.
Implement agent observability using tracing, metrics, logs, prompt and model version tracking, tool-call records, evaluation results, token and cost tracking, user feedback, incident evidence, and action audit trails.
Build and maintain evaluation frameworks using automated tests, curated test sets, simulation, replay, benchmark tasks, regression suites, model-based evaluators, human review, and operational-outcome measures.
Evaluate agents on task completion, factuality, groundedness, retrieval quality, diagnostic accuracy, recommendation quality, tool-selection accuracy, tool-execution correctness, policy compliance, latency, cost, safety, and user or operator satisfaction.
Partner with the Kubernetes and custom scheduler team to consume and explain queue state, placement rationale, topology information, capacity signals, workload lifecycle events, policy outcomes, and performance data.
Partner with the Model-to-Grid product, inference, Platform, SDI, Security, UX, and global operations teams to turn agent capabilities into clear product workflows, production releases, runbooks, and measurable user and operational outcomes.