OptScale AI doesn't run your agents — we govern them. Register the agents you already built with LangChain, CrewAI, or custom code. Apply cost and time limits, detect anomalies in real time, block unauthorized MCP access, and flag insecure operations as they happen
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Sound Familiar?
You've built agents with LangChain, CrewAI, or custom code. They're running in production — but nobody is watching what they actually do
An agent enters a recursive loop and burns through your monthly LLM budget in an afternoon. There are no per-agent caps on cost, tokens, or execution time, and you only find out after the invoice arrives.
Agents fall into recursive tool-call patterns, drift from baseline behavior, or burst tokens unexpectedly. By the time someone notices, damage is already done.
Your agents call any MCP server or vector store they can reach. There's no whitelist, no boundary, no way to stop an agent from touching data or systems it has no business accessing.
Large refunds at 3 AM. Production data deletions. Privilege escalation attempts. Without real-time alerting, these slip through until someone files a ticket the next morning.
Every agent is its own black box. You can't see which agents are running, what they cost, how often they fail, or how they behave compared to last week. No dashboard, no comparison, no signal.
Compliance asks: "What did the agent do, when, and why?" You don't have a clean answer. Logs are scattered across frameworks, partial, or missing the context that matters.
Why your controls miss this
The controls most teams deployed over the last two years were built for the chat completion — one request in, one response out. An autonomous agent inverts almost every property that made the completion governable
One request in, one response out
One goal in, an unknown number of steps out
Stateless — nothing persists after the reply
Stateful — carries memory and context across calls
Returns text; it does not take actions
Takes actions: calls tools, APIs, databases, other agents
Cost is bounded and predictable per call
Cost is open-ended — it decides how hard to try
Can be inspected before it ever runs
Its real behaviour only exists at runtime
We've spent two years hardening the wrong layer. We filter what the model says — we don't govern what the agent does.
In production today
The governance gap isn't abstract. It surfaces as four recurring failure modes — each with a mechanism, and a reason the tooling you already own doesn't catch it
One goal fans out into hundreds of model calls. Retries multiply, reasoning loops re-query, and an overnight run quietly consumes a month of budget.
Why your tools miss it: per-call rate limits cap one request — they can't see that a single agent goal just triggered nine hundred of them.
A function returns; a loop has an exit condition. An agent has neither guaranteed — two agents ping-pong, a re-planning loop never converges, the agent drifts and keeps generating.
Why your tools miss it: uptime monitoring confirms the service is healthy — and a perfectly healthy service can loop forever.
The agent retrieves from an internal store, then calls an external model — each step legitimate, the combination carrying your data out. Memory and tool results ride along into third-party calls.
Why your tools miss it: guardrails scan the user's prompt. The dangerous payload is the one the agent assembles itself mid-run.
The agent reaches a tool, MCP server or data store nobody approved. It discovers capability outside the sanctioned set and chains harmless calls into an action no one signed off on.
Why your tools miss it: CI/CD governs the code you shipped, not which tools the agent chooses to invoke at runtime.
In production today
You can't trust an agent to govern itself, and you can't bolt a controller onto every framework. Whatever an agent is built on, its model calls, tool calls and retrievals all flow outward through one point: its outbound traffic. Put the control there.
We've spent two years hardening the wrong layer. We filter what the model says — we don't govern what the agent does.
1
Every agent declared, owned and named before it runs. Nothing reaches production unregistered.
2
Hard ceilings on cost, time and recursion, set per agent and enforced live.
3
An allowlist of the tools, MCP servers and stores each agent may reach — denied by default.
4
Live detection of loops, drift and token bursts, evaluated at the goal level, not the call.
5
Every call, tool use and decision in a tamper-evident log — for incident response and audit.
Governance as a by-product
These five controls are operational reliability measures first — but they're largely the same work as compliance. The overlap with deployer obligations is direct.
Registration & identity
Establishes ownership and meaningful human oversight
Runtime limits & authorisation boundary
Concrete forms of operational control over the system in use
Anomaly detection
Monitoring how the system behaves in use
Immutable audit trail
Record-keeping, logging and traceability for audit
This page is not legal advice. Whether and how specific obligations apply depends on your role, use cases and how a system is classified — treat this as a starting point for a conversation with your own counsel.
No vendor required
Every step can be started with the team and tools you already have. The first one alone will tell you whether you have a problem.
1
Inventory. Ask every team for a list of the agents they have in or near production. Expect surprises — the list itself is the wake-up call.
2
Route through one path. Put agent traffic behind a single gateway so every model, tool and retrieval call is visible in one place.
3
Set hard limits. Cap cost, time and recursion per agent. Begin strict and relax deliberately as you gather evidence of normal behaviour.
4
Allowlist capability. Define which tools, MCP servers and data stores each agent may reach. Deny by default; permit explicitly.
5
Log everything. Capture every call and decision in an immutable trail — for debugging incidents today and for audit tomorrow.
Free whitepaper
The full vendor-neutral framework for engineering, security and platform leaders — the four failure modes, the five controls, the EU AI Act mapping, and the Monday-morning steps. Yours to start using this week.
MCP & Vector Store Registry
OptScale AI maintains the registry of MCP servers and vector stores your agents are allowed to call. Whitelist per agent, block everything else, log every access attempt. 20+ services supported and growing monthly.
New connectors ship monthly. Enterprise customers can request priority connectors or build custom ones with the MCP Server SDK
Register & Govern
OptScale AI doesn't run your agents — we control them. Bring any agent you've built with LangChain, CrewAI, AutoGen, or custom code. Once registered, every agent operates under enforced cost limits, time limits, recursion limits, and authorized resource boundaries. Auto-stop kicks in the moment a limit is breached.
🤖 Bring any agent — LangChain, CrewAI, AutoGen, or custom code. OptScale AI registers and monitors them all
💰 Hard per-agent caps: max cost per task, max tokens, max execution time, max recursion depth — with auto-stop on breach
🔐 Whitelist authorized MCP servers and vector stores per agent. Unauthorized access attempts are blocked and logged
📄 Full audit trail of every agent action — who, what, when, why — compliance-ready and replayable
🔴🟡🟢 Agent Registry — agents.optscale.ai
● Monitored
LangChain
Production
Owner: sales-eng
$25/day · $500/mo · 60s/task · max 5 recursion depth
Salesforce
HubSpot
Slack #sales
Loops: on · Token bursts: on · Drift: on · Tool-call patterns: on
After-hours writes · Large refunds · Prod deletions · Privilege escalations
Real-time Detection
Loops, drift, unauthorized access attempts, and risky operations — detected, blocked, and logged in real time. Not after the fact, not in next week's report.
🔴🟡🟢 Live Alerts — agents.optscale.ai/alerts
5 events in last hour
Code Reviewer v3.1 · depth 12, max 5 · auto-stopped after 4.2s
BLOCKED
Support Agent v2.0 tried to call HR-Data MCP · not in whitelist
BLOCKED
Research Writer v1.8 · $26.40 / $25.00 daily cap · auto-stopped
STOPPED
Finance Agent attempted refund $4,200 · outside business hours
FLAGGED
Sales Analyst v2.4 · token usage 3.2x baseline · investigation queued
ALERTED
OptScale AI watches every registered agent continuously. Recursive loops are caught mid-execution and stopped before they burn budget. Unauthorized MCP calls are blocked at the gateway, not discovered weeks later in logs. Cost limits trigger auto-stops the moment they're breached. Risky operations — after-hours writes, large refunds, production deletions, privilege escalations — are flagged the instant they happen. Every event lands in a tamper-evident audit trail, ready for compliance and replayable for post-incident review.
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