Runaway loops
An agent repeats the same action, call, or input until latency and token spend grow.
Runtime recovery for production AI agents
Stop runaway AI agents before they hit production tools.
Nxolaryn is a lightweight runtime recovery layer for engineering teams using AI agents that call APIs, databases, or internal tools. It detects loops, schema drift, state loss, latency spikes, token waste, and risky actions, then routes each event to an approved pause, block, retry, fallback, or human-review path.
No public payload upload
Sidecar-style control layer
Reviewable decision records
Example runtime decision
Loop detected. Execution paused. Review record created.
Instead of letting an agent keep retrying a broken tool call, Nxolaryn routes the workflow to the recovery path your team already approved.
DetectRepeated action
RecoverPause or fallback
RecordNon-sensitive event
The problem
Agent failures are becoming execution risks.
When AI agents call real tools, a small failure can become repeated execution, unnecessary spend, downstream side effects, or a human-review gap.
Broken tool boundaries
Malformed arguments, schema drift, and missing state create retries instead of resolution.
Unclear approval paths
Teams need a clear answer to what the agent is allowed to do next.
How it works
Detect. Recover. Record.
Nxolaryn turns agent failure modes into approved runtime decisions that engineering, security, and operations teams can review.
Detect the runtime failure
Watch configured boundaries for repeated calls, invalid tool arguments, state gaps, latency thresholds, token spikes, or review-sensitive actions.
Apply the recovery path
Pause, block, retry once, fallback, normalize a boundary, or route to human review based on the customer-approved policy.
Preserve the decision record
Keep a compact, non-sensitive record of the trigger, policy result, action taken, and review metadata.
Topology
A clean control layer between agent intent and tool execution.
The recovery layer is designed to sit at the execution boundary—not as another generic dashboard after the failure already happened.
Agent orchestrator
Nxolaryn recovery layer
LLM, APIs, databases, and tools
Product surface
Focused controls for high-risk agent behavior.
Loop limit
Stop repeated execution before it becomes spend, latency, or incident noise.
Schema boundary
Catch malformed tool arguments before an agent retries a broken contract.
State handoff check
Pause workflows when required state is missing between steps.
Latency breaker
Route stalled calls to fallback or review before the workflow hangs.
Token boundary
Make runaway context and retry spend visible before it compounds.
Approval gate
Pause sensitive actions until the right human or system approval path is reached.
Implementation evidence
Clear runtime decisions without homepage code clutter.
Public examples are intentionally synthetic and non-sensitive. Approved evaluations require written scope before any production-adjacent material is reviewed.
Agent repeats a tool call
The configured loop threshold is crossed before downstream effects escalate.
Execution pauses automatically
The workflow routes to stop, fallback, limited retry, or human review.
A reviewable event is created
The team receives the trigger, policy result, action taken, and non-sensitive metadata.
Founder
Built by Hasti Shams.
Nxolaryn is founder-led from Los Angeles with an engineering-first focus on agent failure modes, runtime boundaries, and practical recovery paths.
Next step
Start with one AI-agent workflow.
Bring one workflow, one failure boundary, and one recovery question. Do not send logs, prompts, credentials, source code, customer records, or production payloads through public channels.