Agentic AI Production Readiness Diagnostic

Identify your infrastructure gaps before your agents hit production load.

Question 1 of 8

Can your infrastructure handle 50+ concurrent agent sessions with parallel tool calls without rate limit cascades?

Netomi's agents collapsed when parallel tool calls across dozens of sessions hit rate limits. Without concurrency management and rate limit pooling, you'll face the same cascading failures under enterprise load.

YesNoQuestion 2 of 8

Do you have persistent state management for workflows that extend beyond a single LLM context window?

Complex reasoning chains degrade when context limits are reached. Production agents need state stores that persist workflow progress across multiple LLM calls to prevent agents from 'losing track' mid-process.

YesNoQuestion 3 of 8

Can you trace and debug failures deep in multi-step agent workflows without 'digital archeology'?

Without instrumentation for multi-step reasoning chains, debugging production agent failures becomes impossibly opaque. You need observability that captures each tool call, decision point, and state transition.

YesNoQuestion 4 of 8

Do you have governance controls to constrain what tools agents can access and what actions they can take?

Production agents need guardrails. Without access controls and action policies, autonomous agents pose security and compliance risks that block enterprise deployment.

YesNoQuestion 5 of 8

Can you predict and control agent costs before running workflows at scale?

Agent workflows with unpredictable token usage and tool call chains can generate runaway costs. Production systems need cost estimation and budget limits per workflow.

YesNoQuestion 6 of 8

Do you have retry logic and error recovery for failed tool calls in agent workflows?

Tool calls fail—APIs timeout, rate limits hit, external services error. Without automated retry strategies and graceful degradation, individual failures cascade into workflow failures.

YesNoQuestion 7 of 8

Can you version and rollback agent configurations without redeploying code?

Agents are prompt-driven systems that change frequently. Production infrastructure needs configuration versioning and instant rollback when agent behavior degrades.

YesNoQuestion 8 of 8

Do you have metrics on agent success rates, latency, and tool call patterns in production?

You can't improve what you don't measure. Production agent infrastructure requires real-time metrics on completion rates, reasoning step counts, and performance by workflow type.

YesNo

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