An agentic and application runtime turns model-backed requests into controlled work. It defines task boundaries, resolves actor and tenant context, assembles allowed context, routes models, brokers tools, enforces policy, manages memory and approvals, recovers from failure, evaluates outcomes, and preserves evidence.
Key takeaways
- An inference engine produces model outputs; an agentic application runtime produces controlled work.
- The model proposes. The runtime validates and authorizes. Accountable users or systems approve when consequences require it.
- State, authority, tools, memory, recovery, and evidence must not exist only as natural-language prompt text.
Definition
The runtime’s unit of execution is a task or request that may contain several model calls, tool operations, waits, validations, and state transitions. It owns the boundary between probabilistic generation and consequential application behavior. This layer can be implemented using agent frameworks, workflow engines, policy systems, sandboxes, registries, and telemetry rather than one monolithic product.
Boundary with inference and frameworks
An inference engine owns weights, generation state, token scheduling, and model output. An agent framework provides abstractions for agents, prompts, tools, handoffs, and control flow. An application runtime owns operational semantics: durable task identity, authority, state, deadlines, idempotency, isolated execution, approvals, recovery, evidence, and domain integration.
Some frameworks provide portions of runtime behavior. Classify the actual deployed responsibilities rather than assuming the product label.
Request and task boundary
A versioned request contract should include request and correlation identifiers, UTC timestamp, deadline, actor, tenant, task type, risk, input, permissions, context policy, model-route constraints, allowed tools, memory policy, budget, approval policy, output contract, trace settings, classification, and retention. Admission fails before work begins when required authority or compatibility is missing.
[ar_contract type=”runtime”]
Context and model routing
The runtime selects a minimal, permitted context projection and records provenance, freshness, exclusions, and token budget. It chooses a model deployment using capability, latency, cost, data region, provider, precision, and fallback constraints. Route selection is a policy decision, not a hard-coded model string hidden in product code.
Tool brokerage and authorization
[ar_diagram id=”tool-authorization-sequence”]
Tool use follows observe → plan → validate → authorize → execute → record → stop or continue. A model may request a tool, but it does not receive unrestricted credentials or execute arbitrary text. The runtime validates typed input, resolves a short-lived credential, enforces resource and side-effect scope, applies timeout and idempotency, and validates the result.
Classify tools as read-only, reversible write, irreversible, financial/high-impact, external communication, code execution, or administrative. Approval and evidence requirements increase with consequence.
Memory policy
Memory is typed and governed. Working memory supports the active task; project memory stores approved constraints and decisions; user memory stores consented preferences; episodic memory records selected prior events; prohibited memory defines data that must not be retained. Every memory write has source, scope, reason, confidence, retention, correction, and deletion behavior.
[ar_diagram id=”memory-boundary-model”]
Approvals and human review
Approval is an explicit runtime state, not a chat message. The approval package names the proposed action, resources, expected side effects, supporting evidence, alternative, rollback or compensation, expiry, and approver authority. The task releases unnecessary compute while waiting and revalidates external state before resuming.
Recovery and compensation
Checkpoints capture task state and references to artifacts, not necessarily raw prompts. Retry requires an eligible operation and remaining budget. External writes use idempotency keys; partially committed work is reconciled; reversible effects expose compensation. Recovery history remains visible rather than overwritten by the final successful attempt.
Evidence and evaluation
Evidence correlates the request, context, model route, tool invocations, policy decisions, approvals, side effects, artifacts, failures, retries, recovery, evaluation, and final output. Sensitive raw input is minimized or represented by protected references and hashes. Evaluation measures task success, safety, cost, latency, recovery, and evidence completeness—not output fluency alone.
[ar_evidence_ledger]
When to use an agentic runtime
Use one when a task spans multiple steps, persists beyond one request, invokes tools, changes external state, needs human review, crosses tenant or data boundaries, must recover, or requires evidence. Use a simpler model call or deterministic workflow when the task is read-only, low consequence, short-lived, and fully specified.