Loops and hangs
Identify repeated calls, stalled steps, runaway retries, and workflows that do not complete cleanly.
Reliability diagnostics for production AI agents
Make production AI agents more reliable.
Nxolaryn helps engineering teams find recurring failure patterns in AI-agent workflows: loops, lost state, broken tool calls, latency bottlenecks, and avoidable token spend.
Redacted logs only
No passwords
No admin access
Engineering-ready report
The problem
Agents work in demos. Production is different.
Production agents are not just prompts. They depend on models, APIs, tools, memory, state, retries, permissions, latency, and cost controls.
When something fails, teams often have traces but still need to understand the root cause and decide what to change. Nxolaryn focuses on that gap: turning failure evidence into a practical remediation plan.
Tool-call failures
Review bad arguments, schema drift, timeout patterns, API errors, and missing fallback handling.
Cost and latency waste
Spot repeated context, unnecessary model calls, slow steps, and avoidable token usage.
From failure traces to repair decisions.
Nxolaryn is starting as a focused diagnostic service. Over time, repeated failure patterns become software modules for safer retries, state checkpoints, schema validation, and reliability reporting.
L
Loop review
Find repeated calls and recommend stop conditions or escalation rules.
S
State review
Identify where state is lost and where checkpointing would reduce restarts.
G
Schema review
Flag malformed arguments, schema mismatches, and tool contract issues.
R
Retry review
Recommend retry limits, fallback paths, and human escalation points.
T
Token review
Find repeated context, bloated prompts, and unnecessary model calls.
E
Review record
Summarize what failed, what changed, and what still needs engineering approval.
Why teams use it
Your engineers should not be full-time agent firefighters.
For engineering leaders
Prioritize the agent failures that are costing time, reliability, and customer confidence.
For AI platform teams
Move from scattered debugging to a consistent reliability review process.
For operations teams
Understand why customer-facing workflows fail and what needs to be fixed first.
For security and legal reviewers
Keep a scoped record of the failure evidence reviewed and the remediation plan recommended.
Initial offer
48-Hour Agent Reliability Diagnostic
Send one production or near-production AI-agent workflow’s redacted traces, failed runs, tool-call errors, latency data, and token usage. Nxolaryn returns a concise report with the most likely failure patterns and recommended next fixes.
- Failure pattern analysis
- Loop, state, tool-call, latency, and token review
- Engineer-readable remediation plan
- Reliability score and optional ongoing support plan
Nxolaryn provides diagnostic and remediation-planning support. It does not provide legal, compliance, cybersecurity certification, or uptime guarantees.
Designed for a low-risk first review.
The initial diagnostic is designed to start from redacted, exported evidence. Nxolaryn does not need production credentials, administrator access, API keys, customer databases, or sensitive records to begin a scoped review.
Redacted evidence
Use sanitized traces, screenshots, logs, and error outputs.
Defined scope
Review one workflow at a time, with clear boundaries and expected deliverables.
Human approval
Recommended changes should be reviewed by the customer’s engineering or security team before deployment.
Have an AI agent that works in demos but fails in real workflows?
Request a 48-hour diagnostic and get a clearer view of what is breaking and what to fix first.
Request diagnostic