Compare
Cheqpoint vs the alternatives
Most teams ship agents with no control layer, bolt on prompt filters that miss novel attacks, or spend weeks building their own gate. There's a better option.
Start free - no card needed →Cheqpoint
Purpose-built
Build it yourself
Custom dev work
Do nothing
Hope for the best
Core
Execution blocks until a human approves - agent cannot proceed without a decision
Reviewer can modify the payload before approving - not just approve or reject
Works with any agent framework - LangChain, CrewAI, OpenAI, AutoGen, raw HTTP
Human approval inbox
API for AI agents to submit requests
Auto-decision rules engine
Real-time Slack / email alerts
Response time targets (SLA timers)
Request replay
Security
Execution blocks until a human approves - not a log, a lock
Behavioral baselining - risk score generated from agent history, not trusted from caller
Blocks prompt injection - action cannot execute even if agent is manipulated
Kill switch - suspend an agent instantly via dashboard or API
Agent anomaly detection - volume spikes, unusual times, new action types
SIEM forwarding - Datadog, Splunk, Sentinel, Elastic, CrowdStrike
CEF export for SIEM forensics and incident investigation
Audit & Compliance
Full audit trail with decision reasons
EU AI Act / GDPR / SOX export
Structured decline reasons
Two-person integrity checks
Session tokens for expiring Assistant access
Integrations
Zapier / n8n webhook support
LangChain / OpenAI SDK
PagerDuty escalation
Slack interactive Approve/Decline buttons
Team & Scale
Multi-reviewer routing & load balancing
Review groups (queues)
Kill switch per AI agent
Per-Assistant action type and amount limits
AI confidence and risk scoring
Risk & policies dashboard
Setup & Maintenance
No infrastructure to maintain
Integrate in under 10 minutes
Managed updates & new features
14-day free trial
Approaches
Automated guardrails vs. an approval gate
Prompt-filtering tools (Lakera, Guardrails AI, Prompt Security, Robust Intelligence and similar) try to detect a bad action and block it automatically. Cheqpoint takes a different position in the stack - it holds the action until it's cleared, and scores risk from the agent's own behaviour. They're complementary: filters reduce noise, Cheqpoint is the backstop that can't be talked past.
Automated guardrails & filters
Detect & auto-block at the model layer
- Scans prompts and outputs for known-bad patterns
- Blocks automatically - a mistaken block or miss is invisible to a human
- A novel injection it wasn't trained on passes through
- No human in the loop - nobody decides, nobody is accountable
- No behavioural baseline - can't catch a compromised agent acting "normally"
- Little audit value for a regulator - a filter verdict isn't a decision record
Cheqpoint approval gate
Hold & decide at the action layer
- Blocks execution of the action itself - the agent physically cannot proceed
- A human (or a policy you wrote) makes the call - explicit accountability
- Fail-safe: if no one approves, nothing fires - even an unknown attack is contained
- Behavioural baselining flags volume spikes, off-hours, and first-seen actions
- Kill switch stops a compromised agent instantly, mid-flight
- Every decision logged with who / what / when / why - SIEM-forwarded, regulator-ready
< 10 lines
of code to integrate
One SDK call in Python or Node.js. No infrastructure changes, no bespoke queuing system.
Full audit trail
out of the box
Every decision logged with who, what, when, and why. CSV export for compliance reviews.
EU AI Act ready
on day one
Approval gate checkpoints, documented decision reasoning, and structured data retention.
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