This checklist consolidates the architecture and operations practices discussed across this cluster into a single working reference, organized so a team can verify production operational readiness systematically before and after an AI system reaches significant scale.
Architecture and boundaries
Confirm the system's architecture draws explicit boundaries between model, tool, and data layers, with the model treated as producing a proposal rather than a final action. Confirm policy enforcement is placed at the tool-call boundary as the primary enforcement point, with content assembly and output validation as supporting layers. Confirm security boundaries hold correctly across component version changes, not dependent on assumptions specific to a particular model or tool version.
Fail-safe and availability design
Confirm every security-relevant dependency has explicit, tested fail-closed behavior by default, with any fail-open exception explicitly documented and justified. Confirm the security control infrastructure itself has appropriate redundancy and capacity planning, given that fail-closed design makes its own availability the application's availability. Confirm gateway or enforcement infrastructure latency has been measured and optimized against an explicit target.
Logging, traceability, and tamper-evidence
Confirm an audit ledger connects identity, decisions, and outcomes with a consistent structure across every contributing source. Confirm the ledger is append-only or otherwise tamper-evident, with restricted write access separate from broader read access. Confirm observability captures the full step sequence for agentic workflows, not only request-level metrics.
Monitoring and detection
Confirm production monitoring layers AI-specific signals, policy decision rates, behavioral baselines, content patterns, provider health, on top of conventional infrastructure monitoring. Confirm per-agent behavioral baselines exist and are used to detect meaningful deviations. Confirm specific, actionable security metrics are tracked, not a single aggregated score.
Network and execution containment
Confirm outbound network access is restricted to known, necessary destinations for model providers and tools, with egress control applied specifically against exfiltration paths. Confirm any code execution capability runs in an isolated, resource-limited, ephemeral sandbox. Confirm service-to-service authentication correctly propagates the original requesting identity through every hop.
Approval and human oversight infrastructure
Confirm approval gates present specific, actionable context and route requests appropriately by risk category. Confirm approval gates have explicit, fail-closed expiration and tested escalation paths. Confirm kill switches exist at appropriate granularity, are tested directly and periodically, and have pre-authorized, fast access for whoever needs to use them.
Incident readiness
Confirm AI-specific incident response procedures exist for prompt injection, data leakage, and tool abuse scenarios specifically. Confirm a detailed, specific, and current operations runbook exists for recurring scenarios, tested through tabletop exercises. Confirm lessons from real incidents and exercises are fed back into runbook and architecture improvements.
Scale operational rigor to system risk tier
Following the risk classification discussed in governance guidance, a low-risk internal tool needs lighter operational infrastructure than a high-risk, high-volume customer-facing agent. Apply the full weight of this checklist to systems where the consequence of an operational gap is genuinely severe, and a proportionally lighter version to systems where it is not, rather than either uniform minimal readiness or uniform maximum investment regardless of actual risk.
Revisit this checklist as systems evolve
A system's operational readiness assessed against this checklist at launch does not remain accurate indefinitely as the system's scale, tools, and usage patterns evolve. Revisit relevant sections when a system's capabilities expand significantly, its traffic grows substantially, or its risk classification changes, rather than treating an initial assessment as permanently valid.
Use this checklist at more than one point in a system's life
Run this checklist before a system's initial production launch, again after any significant expansion of its tools or data access, and again on a recurring schedule proportional to its risk tier, rather than treating a single pre-launch pass as sufficient for the system's entire operational lifetime. Each of these checkpoints tends to surface a different category of gap: pre-launch review catches design-time omissions, post-expansion review catches capability growth that outpaced its original operational controls, and recurring review catches the slow drift that accumulates in any system nobody is actively pruning back toward its original, narrower scope.
Where TELEON fits
Several items on this checklist, tool-call boundary enforcement, fail-closed policy behavior, tamper-evident audit logging, and approval-gate implementation, map directly to what TELEON's runtime policy enforcement and audit trail provide when deployed through the gateway or supported middleware. The remaining items, architecture design, network controls, sandboxing, observability tooling, and runbook content, are engineering and operational work that a runtime tool supports with evidence and enforcement but does not build on its own.
The checklist in short form
- Explicit architectural boundaries between model, tool, and data
layers.
- Fail-closed default behavior with tested, redundant enforcement
infrastructure.
- Tamper-evident audit ledger with full step-level observability.
- Layered, AI-specific production monitoring and specific security
metrics.
- Network egress control and sandboxed execution for dynamic actions.
- Well-designed approval gates and tested, accessible kill switches.
- AI-specific incident response procedures and a current operations
runbook.
- Operational rigor scaled to and revisited against system risk tier.
Production operational readiness for an AI system depends on treating architecture, monitoring, and incident response as integrated concerns built deliberately from the start, not as separate afterthoughts added once a system has already reached a scale where their absence becomes a genuine liability.