Agent-Native Infrastructure
Engineer the runtime, memory, observability, queueing, evaluation, and policy layers needed to operate agent systems reliably in production.
Agent-native infrastructure is the production layer that makes agent systems maintainable. It is not enough to have prompts and tools. Teams need scheduling, retries, memory, identity, access control, observability, replay, evaluation, secrets, and rollout patterns designed specifically for agent behavior.
We help teams move beyond fragile point solutions by creating the platform capabilities that let many agent workflows run safely across a shared operational model. That includes queueing, memory, telemetry, experimentation, approvals, and policy services.
Shared services agent teams need before the roadmap scales
Queues, workers, schedulers, retries, handoffs, and durable state for long-running agent workflows.
Design state stores, vector retrieval, trace capture, artifact storage, and session models.
Apply secrets management, scoped tool access, approvals, audit trails, and environment separation.
Measure correctness, drift, failure modes, runtime cost, latency, and workflow outcomes.
Programs that need an agent-native platform layer
Give multiple teams a common runtime, evaluation model, and operations layer for agent development.
Run thousands of queued tasks with retries, routing, and policy-aware execution.
Add auditability, approvals, access control, and reliable replay into the infrastructure itself.
Coordinate agents, memory, and telemetry across more than one workflow or business function.
How TensorBlue moves the build forward
Understand current LLM tooling, workflow demands, reliability expectations, and governance requirements.
Design the queueing, memory, retrieval, policy, and observability layers around the target workloads.
Implement shared services for execution, telemetry, evaluation, replay, and access control.
Define SLOs, incidents, rollout patterns, and platform onboarding for agent teams.
Execution plane, state plane, control plane
Agent-native platform stack
- Ingress
- Tasks from apps, operators, schedules, or event streams.
- Execution plane
- Queues, workers, schedulers, concurrency controls, and retries.
- State plane
- Memory, retrieval, trace logs, artifacts, and result storage.
- Control plane
- Policy, approvals, identity, secrets, and environment rules.
- Feedback plane
- Evaluation, observability, cost tracking, and incident workflows.
Sample pseudocode
task = enqueue(agentJob) state = hydrate_memory(task) result = run_agent(task, state) record_trace(result) score_eval(result)
What changes when the delivery is built correctly from the start
Prompt infrastructure only
Agent-native infrastructure
Infrastructure determines whether agent systems scale or stall.
Agent reliability is an operations problem as much as a model problem.
Questions teams ask before the work begins
Yes. Agent workloads need different controls around memory, retries, evaluation, policy, and replay than standard request-response apps.
Agent-Native Infrastructure
Clear scope, commercial framing, and delivery outputs so the engagement is easy to evaluate.
Services that pair naturally with this one
Most strong delivery programs connect this capability to adjacent systems, platform layers, or revenue surfaces.
Build the agent behavior on top of the right runtime, memory, and evaluation foundation.
Run browser-native agents on a platform built for retries, traces, and policy.
Back enterprise workflows with a shared platform for governance and scale.
Need the infrastructure beneath serious agent systems?
We can design and build the runtime, memory, observability, and governance layers your agent roadmap depends on.