Agent runtime that knows where it runs, what it costs, and what it emits.
- Private agent runtime inside your tenancy with IAM, audit, and tracing
- Inference batch scheduling in cleaner-grid windows
- Model- and provider-neutral with Coretex memory integrated
- Carbon-aware placement constrained by your compliance rules
What makes AI Agents Infrastructure different for regulated teams
Five runtime controls that public-cloud agent platforms cannot provide together.
Private agent runtime with isolation evidence
Agents run inside your tenancy on dedicated compute, not shared public-cloud infrastructure. Workload isolation is enforced per agent class, with IAM, audit, and tracing attached to every run so sensitive and non-sensitive workloads never share a boundary.
Model- and provider-neutral by design
Route inference across providers and models on carbon, cost, latency, and compliance signals at once. No provider lock-in, so you keep both operational flexibility and the ability to place each call where it runs cleanest and cheapest.
Coretex memory integrated, per-tenant isolated
Agent memory runs on Coretex with per-tenant, per-matter isolation and a full audit ledger of every model call. Context never bleeds across clients, matters, or departments, so compliance-sensitive deployments share infrastructure without violating isolation.
Carbon-aware inference scheduling
Training runs, embedding generation, and large inference batches are scheduled into cleaner-grid windows. Per-workload Scope 2 records with evidence labels are generated automatically, constrained by your compliance rules before any placement.
A managed path to Private Cloud
Start with a governed agent runtime inside your tenancy. As regulatory requirements demand fully dedicated infrastructure, agent workloads can migrate to Private Cloud. The orchestration layer spans both, so evidence and controls travel with the workload.
How AI teams use agent runtime orchestration
Legal AI agent with per-matter isolation and audit trail
The legal AI agent runs on shared public cloud with no per-matter isolation. Model calls are not logged at the level required for legal defensibility. The firm cannot prove which model was used for which matter at which time.
GREENPOW runs the agent in an isolated namespace per matter. Every model call is logged with model version, token count, timestamp, and compliance classification. The audit trail is exportable per matter for client review.
AI batch scheduling for enterprise model training
Training runs execute immediately on available GPU capacity. The MLOps team has no mechanism to route training to lower-carbon regions or cleaner-grid windows. Carbon per training run is unknown.
GREENPOW schedules training runs to the next eligible clean-grid window in the lowest-carbon compliant region. Carbon per training run is measured and attributed. The evidence is attached to each run record in the model registry.
Multi-provider inference routing for cost and carbon optimization
The platform team routes all inference to a single provider by default. They cannot compare carbon, cost, and latency across providers in real time. Model provider lock-in is structural.
GREENPOW scores eligible inference endpoints on carbon, cost, and latency per request type. Routing adapts in real time as conditions change. Provider lock-in is eliminated at the runtime routing layer.
AI agent with Coretex operational memory for enterprise knowledge work
The enterprise agent has no persistent memory between sessions. It re-asks for context every run, producing inconsistent outputs and frustrating users who expect continuity across agent interactions.
GREENPOW integrates Coretex per-tenant memory into the agent runtime. The agent accumulates organizational knowledge across sessions within strict per-tenant isolation. Context is reused without re-prompting.
How GREENPOW AI Agents Infrastructure compares
Against hyperscaler AI platforms, generic MLOps tooling, provider-native inference, and bare-metal GPU hosting.
| GREENPOW AI Agents | Hyperscaler AI platform | Generic MLOps | Provider-native inference | Bare-metal GPU | |
|---|---|---|---|---|---|
| Private per-tenant runtime isolation | - | Partial | - | ||
| Carbon-aware inference routing | - | - | - | - | |
| Model-neutral routing | - | Partial | - | ||
| Per-run model call audit trail | Limited | Limited | - | - | |
| AI Act compliance controls | - | - | - | - | |
| Coretex memory integration | - | - | - | - | |
| Batch scheduling for clean-grid windows | - | - | - | - | |
| Per-run carbon evidence label | - | - | - | - |
How AI Agents Infrastructure fits in the GREENPOW portfolio
AI Agents Infrastructure is one layer of a connected infrastructure stack. Understand where it sits and when to combine it.
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Moving agents from demos to production exposes every gap in your infrastructure
Agent systems in production face a different set of problems from demos. The infrastructure layer makes or breaks governed, auditable, cost-controlled AI operations.
Agent runtimes run on shared infrastructure with no workload isolation
Most agent deployments run on public cloud compute with no workload-level isolation, no model call attribution, and no audit trail that satisfies compliance. The same infrastructure handles sensitive and non-sensitive workloads without differentiation.
Inference costs are unpredictable and unattributed
Token consumption and compute cost per agent run are not tracked at workload level. Teams cannot attribute inference cost to specific agents, use cases, or tenants. Budget overruns are discovered after the fact.
AI batch jobs run immediately, not in clean-grid windows
Training runs, embedding generation, and large inference batches execute as soon as resources are available. There is no scheduling intelligence connecting grid carbon state to batch execution timing.
Model calls are not audited at the level compliance requires
AI Act obligations for high-risk systems require model call logs, human oversight mechanisms, and decision provenance. Standard cloud infrastructure does not produce these at the runtime level without custom instrumentation.
Provider lock-in limits both operational flexibility and carbon optimization
Agent runtimes tied to a single provider forfeit the ability to route inference across providers on carbon, cost, and performance signals simultaneously.
Agent memory is stateless or uses shared vector stores with no isolation
Without per-tenant memory isolation, agent context bleeds across clients, matters, or departments. Compliance-sensitive agent deployments cannot share memory infrastructure without violating isolation requirements.
How agent runtime orchestration operates
The orchestration lifecycle for AI agent workloads runs the same eight stages with inference scheduling, compliance controls, and memory integration.
Technical deployment surface
GREENPOW AI Agents Infrastructure deploys as a private runtime layer inside your tenancy with model-neutral inference routing and compliance controls.
Private agent runtime inside your tenancy
Agent execution environment isolated within your tenancy with IAM-controlled access, audit logging enabled by default, and per-agent resource quotas. No shared compute with other tenants.
Model-neutral inference routing
Route inference requests across OpenAI, Anthropic, Mistral, self-hosted models, and custom inference endpoints. Routing decisions made per request on carbon, cost, latency, and compliance signals.
Coretex memory integration
Coretex per-tenant memory namespaces integrated into agent runtime. Agents accumulate and retrieve organizational context within strict isolation boundaries. Memory audit ledger included.
AI Act compliance instrumentation
High-risk AI system runtime controls deployed as orchestration-layer components: model call logging, human oversight checkpoints, decision provenance records, and conformity assessment documentation.
Carbon-aware batch scheduling
Embedding generation, training runs, and evaluation batches scheduled into clean-grid windows using GREENPOW signal feeds. Carbon attributed per run with evidence label attached to model registry records.
Observability and cost attribution
Per-agent, per-run, and per-model-call telemetry available via API and dashboard. Cost and carbon attributed at workload level. Integration with Prometheus, Datadog, and custom observability stacks.
Technical questions
Does GREENPOW support all major model providers?
GREENPOW routes inference across OpenAI, Anthropic, Mistral, Cohere, and self-hosted models via compatible APIs. Routing decisions are made per request on carbon, cost, latency, and compliance signals. New providers can be added via configuration.
How does per-matter isolation work for legal AI agents?
Each matter gets an isolated agent runtime namespace with IAM-controlled access, scoped telemetry, and Coretex memory namespace. Model call logs are scoped to the matter ID. Evidence exports are per-matter and exportable for client review without cross-matter data leakage.
What AI Act compliance controls does GREENPOW provide?
GREENPOW provides orchestration-layer compliance controls for high-risk AI systems: model call logging with model version and parameters, human oversight checkpoint instrumentation, decision provenance records, and documentation for conformity assessment. Framework rules are policy-as-code and can be extended.
How does carbon-aware batch scheduling work for training runs?
Training and embedding jobs are submitted to a schedulable queue. GREENPOW projects the 24h carbon and price forecast for eligible compute regions and schedules the run into the next clean-grid window within the configured SLA boundary. The carbon attribution is attached to the run record in your model registry.
Can GREENPOW integrate with our existing MLOps pipeline?
Yes. GREENPOW exposes a scheduling API compatible with standard MLOps tools. Training jobs can be submitted via SDK, CLI, or Kubernetes Job spec. Run records with carbon attribution can be exported to MLflow, W&B, or custom model registries via webhook.
How does Coretex memory integrate with the agent runtime?
Coretex provides per-tenant memory namespaces that the agent runtime reads from and writes to within each agent session. Memory is isolated by tenant - no cross-tenant retrieval is possible. The Coretex audit ledger records every memory write and retrieval with timestamps and agent identity.
Review your AI runtime architecture
Tell us your agent workload profile, compliance requirements, and provider constraints. We map a governed runtime architecture together.