The infrastructure layer
that makes agents production-grade
Isolated execution, structured queues, separated memory layers, full observability, and defined failure paths. The runtime concerns that enterprise teams ask about — answered in the platform, not in your code.
What the agent runtime handles so your code doesn't have to
Each of these concerns has a common pattern in production AI deployments: teams build them as one-offs, inconsistently, per agent. Orchestrik builds them once, at the platform layer.
Each agent runs in its own isolated context
Agents are not shared processes. Each invocation runs in an isolated execution context with its own memory space, credential scope, and connector bindings. A fault in one agent does not affect any other. Resource limits — CPU ceiling, memory cap, wall-clock timeout — are set per agent at deployment time and enforced by the runtime, not by the agent itself.
- Per-invocation execution isolation (no shared state across agents)
- CPU and memory resource caps enforced at the runtime layer
- Configurable wall-clock timeouts with graceful termination
- Retry policy and backoff configured in the control plane
Structured task queues, not ad-hoc calls
Agent work is dispatched through a task queue with defined priorities, deduplication, and idempotency keys. Tasks can be triggered by events, schedules, or upstream agent outputs. The scheduler surfaces queue depth, in-flight count, and stall detection in the control plane dashboard — you can see the backlog before a user notices a delay.
- Priority queues with configurable lanes per agent type
- Idempotency keys to prevent duplicate task execution
- Event-driven, scheduled, and chain-triggered dispatch
- Queue depth and stall-detection surfaced in the control plane
Working memory, session memory, and long-term context — separated
Orchestrik separates three memory layers by design. Working memory is ephemeral — it exists for the duration of a single task and is cleared on completion. Session memory persists within a conversation boundary. Long-term context is grounded from your knowledge sources at retrieval time, not injected wholesale into every prompt. This keeps context windows clean, costs controlled, and recall auditable.
- Working memory cleared on task completion (no residual state)
- Session memory scoped to conversation boundary with expiry
- Retrieval-augmented context from indexed knowledge sources
- Memory access logged as a distinct event class in the audit trail
Structured traces on every agent invocation
Every agent run produces a structured trace: the input, the tools called (in order), the connector invocations with outcomes, the model calls with token counts, and the final output. Traces are stored in the audit store with the same retention policy as the rest of the audit log. You can replay any run from its trace without re-invoking the agent.
- Structured trace per invocation (inputs, tool calls, outputs)
- Per-connector-call logging: agent, action, latency, outcome
- Model call logging: provider, tokens, latency, cost estimate
- Trace replay without re-invoking the live agent
Failure paths are designed, not discovered
The control plane and agent runtime are designed for the failure case. If an agent invocation fails — connector timeout, model error, downstream system unavailable — the platform follows a defined fallback policy: retry with backoff, escalate to a human queue, or fail closed with a structured error. Dead letter queues capture tasks that exhaust their retry budget without data loss.
- Defined retry policy with exponential backoff per failure class
- Dead letter queue for tasks that exhaust retry budget
- Human escalation path configurable per agent and failure type
- Control plane itself runs with multi-node redundancy (non-SaaS: customer-controlled)
Tenant isolation at every layer
In multi-tenant deployments, agent configurations, credentials, audit logs, and runtime state are isolated at the data layer — not just at the application layer. A misconfiguration in one tenant's agent cannot bleed data or context into another. For regulated industries or clients with strict data residency requirements, single-tenant and on-premise deployments are first-class options, not workarounds.
- Data-layer tenant isolation (not just application-layer)
- Per-tenant credential vault with no cross-tenant key access
- Per-tenant audit log partition with independent retention controls
- Single-tenant and on-premise options for compliance or residency requirements
The same runtime, three deployment topologies
The execution model, memory architecture, and observability stack are identical across all three modes. The difference is where it runs and who operates it.
Full control plane and runtime deployed in your data centre or private network. No data leaves your environment.
- Customer-managed infrastructure
- No outbound network requirement
- Air-gapped configurations supported
Deployed into your AWS, Azure, or GCP account as a private stack. You own the VPC, the keys, and the data.
- Customer-owned cloud account
- Private VPC deployment
- Bring-your-own KMS
Fully managed hosted environment with tenant isolation and SOC 2 controls. Fastest path to production.
- Managed infrastructure and upgrades
- Tenant-isolated runtime
- SOC 2 Type II controls
Already have agents? They run here too.
Custom agents connect via REST API or webhook. Orchestrik wraps them with the full infrastructure layer — isolated context, connector access, audit trail — without touching your agent logic. Your agent gets the runtime it needs; your team gets the governance they require.
See the runtime in a live environment
We'll walk through isolation boundaries, trace replay, queue configuration, and HA behaviour with a running instance — not slides.