Circuit Breaker for AI Agents
Stop AI Agents Before They Cost You Millions
Real-world scenarios where FuseGov prevented financial disasters, data breaches, and runaway infrastructure costs—by blocking dangerous AI decisions in milliseconds.
Why Traditional Solutions Fail
API Gateways
❌ No semantic understanding
❌ Can't detect intent ("transfer to my account" with valid credentials looks legitimate)
❌ No AI-specific controls
Prompt Guards
❌ Only inspect LLM inputs/outputs
❌ Can't govern downstream APIs (Stripe, AWS, databases)
❌ No enforcement on tool calls
FuseGov
✅ Intercepts actual API calls
✅ Semantic intent analysis
✅ Works with any agent framework
✅ Automatic failover (no downtime)
The Finance Agent
Prevent "authorized" financial disasters
Stop Hallucinated Payments Before They Clear
Autonomous finance agent with Stripe/QuickBooks API access processing invoices and payments
The Scenario
A LangChain agent tasked with "paying outstanding invoices" hallucinates and attempts to double-pay a $50,000 invoice. All credentials are valid. The intent is wrong.
•Root cause: Agent misread invoice status as "unpaid" when reviewing payment history
•API call:
stripe.charges.create(amount=50000, customer_id="cus_xxx")•Detection time (traditional): 24+ hours (discovered during reconciliation)
•Cost to recover: $50K tied up, 3-5 day refund process, accounting overhead
How FuseGov Stops It
1
Stage 1: Deterministic Payment Limit
FuseGov intercepts the Stripe API call. Stage 1 enforces a hard rule: Any payment >$10,000 requires human approval.
BLOCK: Payment amount ($50,000) exceeds autonomous threshold ($10,000)
2
Stage 2: Semantic Duplicate Detection
The AI-powered semantic layer analyzes the payment context and discovers invoice
INV-2024-1847 was already paid 3 days ago.SEMANTIC_VETO: Duplicate payment detected (invoice paid 2024-12-24)
3
Human-in-the-Loop Escalation
CFO receives Slack notification with full context. Reviews in 30 seconds, rejects payment, corrects agent's invoice status understanding.
Outcome
Prevented $50K duplicate payment (detected in 1.2ms)
Agent continued processing other valid invoices without interruption
Zero false positives on payments <$500 (12,847 processed automatically)
Complete audit trail for SOX compliance (all payment decisions logged in CTR)
Patent-Protected Technology
This scenario demonstrates Claim 1(c) Two-Stage Enforcement from our provisional patent. Stage 1 handles deterministic rules (payment limits) while Stage 2 provides semantic context analysis (duplicate detection)—a unique architecture competitors cannot replicate without infringement risk.
Endpoints governed: Stripe Charges API, QuickBooks Payments API, Internal accounting database
Policy types: Amount thresholds, Duplicate prevention, Velocity limits (max 5 payments/minute)
Agent frameworks: LangChain, AutoGPT, custom agent with function calling
Average latency: 1.2ms (Stage 1 only), 47ms (Stage 2 semantic check)
The DevOps Agent
The circuit breaker for runaway infrastructure
Prevent the $847K AWS Bill
AutoGPT agent with AWS permissions tasked with "optimizing application latency"
The Scenario
Agent decides that spinning up 100x p4d.24xlarge GPU instances ($32.77/hour each) will "reduce latency" for a dataset that would run fine on 2x instances.
•Planned spend: $157/hour (2 instances)
•Agent's plan: $3,277/hour (100 instances) = $78,648/day
•Discovery time (traditional): Next morning when AWS billing alert fires
•Estimated damage: $847K+ over 10 days before manual intervention
How FuseGov Stops It
1
Stage 1: Cost Velocity Limit (Token Bucket)
FuseGov intercepts EC2
RunInstances API call. Calculates estimated hourly cost: $3,277/hour exceeds safety threshold of $500/hour.BLOCK: Hourly cost rate ($3,277) exceeds autonomous limit ($500)
2
Stage 2: Anomaly Pattern Recognition
Semantic analysis compares request against historical patterns. Flags: "100 instances is 50x normal usage. Likely bug or attack, not legitimate optimization."
ANOMALY: Request deviates 5000% from 7-day baseline (μ=2.1 instances, σ=0.8)
3
VP Engineering Approval Required
On-call engineer receives PagerDuty alert with cost breakdown. Reviews agent's reasoning, approves controlled scale-up to 12 instances (sufficient for task, $394/hour).
Financial Impact
Prevented estimated $800K+ in runaway cloud costs
Approved controlled scale-up completed optimization task successfully
Auto-scaling still works for legitimate traffic spikes (within cost limits)
Detection time: 340ms (vs 12+ hours with traditional billing alerts)
Patent-Protected Technology
Demonstrates Claim 1(f) Deterministic Fallback: If Stage 2 semantic analysis times out or becomes unavailable, Stage 1's cost velocity limit still blocks the dangerous request—ensuring zero downtime protection even during system degradation.
Endpoints governed: AWS EC2, Auto Scaling Groups, Lambda, ECS/EKS
Policy types: Cost velocity caps, Instance count limits, Anomaly thresholds (3σ deviation)
Integration: Works with existing auto-scaling—adds safety layer without replacement
Average latency: 0.8ms (Stage 1), 340ms (Stage 2 with anomaly detection)
The Support Agent
Stop PII leaks before they send
Prevent the GDPR Nightmare
E-commerce LangChain agent handling customer support queries with database access
The Scenario
Customer asks: "Can you check if my refund went through?" via prompt injection, the agent is tricked into executing SELECT * FROM customers LIMIT 500and nearly sends 500 customer records including emails, phone numbers, and addresses.
•Attack vector: Prompt injection via customer message
•Data at risk: 500+ customer records (emails, phone, addresses, order history)
•GDPR exposure: Up to €20M fine or 4% of global revenue
•Reputation damage: Mandatory breach disclosure, customer trust collapse
How FuseGov Stops It
1
Stage 1: Query Scope Enforcement
Intercepts database query. Blocks
SELECT * and queries without WHERE customer_id = [context]. Agent can only query the customer who sent the message.BLOCK: Query missing required WHERE clause with customer_id from session context
2
Stage 2: PII Field Restrictions
Even if query were allowed, semantic layer would redact sensitive fields: SSN, credit card numbers, password hashes. Only returns sanitized data needed for support.
REDACTED: 3 fields blocked (ssn_last4, billing_address_full, payment_method_token)
3
Result Set Size Limits
Blocks queries returning >10 rows. Prevents bulk data extraction via
LIMIT manipulation or pagination abuse.Compliance & Security Outcome
Blocked 23 prompt injection attempts in first month (all logged for security review)
Agent still functions normally for legitimate customer queries (98.7% approval rate)
GDPR Article 32 compliant (technical safeguards documented via CTR audit trail)
1.2ms average decision latency (imperceptible to customers)
Patent-Protected Technology
Implements Claim 2(b) Multi-Layer Boundary Evaluation: Query-level scoping (Stage 1), field-level redaction (Stage 2), and result-set limits (Stage 1) work together to prevent data exfiltration even when attackers try multiple bypass techniques.
Endpoints governed: PostgreSQL customer DB, Stripe API, SendGrid email API
Policy types: Query scoping, Field restrictions, Result set limits, PII detection
Agent frameworks: LangChain, AutoGPT, custom agents with database tool access
Compliance: GDPR Article 32, CCPA, SOC 2 Type II, ISO 27001
Database Operations
Prevent accidental mass deletion and data loss
The $2.3M Data Loss Incident
Automated cleanup script with bug executes
DELETE FROM userswithout WHERE clause. Deletes entire production table (2.3M records). Recovery: 6 hours from backups, 4 hours of transactions lost permanently.FuseGov Prevention
✓ Blocks DELETE/UPDATE affecting >1,000 rows without DBA approval
✓ Requires WHERE clause for all destructive operations
✓ Caught before execution, zero data loss
Additional Database Protections
Production safeguards
No TRUNCATE, DROP TABLE, or ALTER TABLE during business hours (enforced time windows)
Query performance limits
Blocks queries estimated to consume >100GB RAM or >60s execution time
Schema change controls
Requires migration review for schema modifications, prevents accidental column drops
Cloud Infrastructure
Automated cost controls and security policies
Common Cloud Governance Scenarios
S3 bucket exposure prevention: Blocks public bucket creation, requires encryption at rest for sensitive data classifications
IAM privilege escalation blocks: Prevents automated scripts from granting admin permissions, requires security team approval
Resource tagging enforcement: Blocks untagged resource creation (cost center, environment, owner tags required)
Multi-Cloud Support
FuseGov's extensible architecture governs cloud operations across providers:
AWS
EC2, Lambda, S3, RDS, ECS
Azure
VMs, Functions, Blob Storage
GCP
Compute Engine, Cloud Run
Kubernetes
Any cluster (EKS, GKE, self-hosted)
Trading & Financial Systems
Prevent Knight Capital-style algorithmic trading disasters
The $440M Software Bug (Knight Capital, 2012)
Deployment error caused trading algorithm to place millions of unintended orders. Lost $440M in 45 minutes. Company never recovered.
FuseGov Trading Protections
Position limits: Block orders exceeding portfolio % allocation
Velocity checks: Max 100 orders/second per strategy
Order value limits: Single order >$5M requires approval
Market condition gates: Suspend during circuit breakers
Compliance Coverage
SEC Rule 15c3-5: Pre-trade risk controls with documented evidence
FINRA 3110: Supervisory procedures for automated trading
MiFID II: Algorithmic trading controls and circuit breakers
Detection latency: 0.7ms average (critical for HFT compliance)
Extensible Architecture
FuseGov's patent-pending two-stage enforcement works across any programmable endpoint. Our initial focus is AI agents and cloud APIs, with expansion planned for 2026.
Current Focus (2025)
✓ AI Agents (LangChain, AutoGPT)
✓ Cloud APIs (AWS, Azure, GCP)
✓ Databases (PostgreSQL, MySQL)
✓ Payment systems (Stripe, PayPal)
Expansion Roadmap (2026)
→ Industrial SCADA systems
→ Energy grid management
→ Healthcare/HIPAA systems
→ IoT device networks
Patent Coverage
⚖️ 19 endpoint types claimed
⚖️ Two-stage architecture
⚖️ Degraded mode failover
⚖️ Filed December 2024
Build Your Custom Governance Solution
Join our design partner program. We'll write policies for your specific AI agents and infrastructure.
🎯
Custom Policy Development
We build governance rules tailored to your agents, APIs, and risk profile
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Zero Cost Pilot (Q1-Q2 2025)
Free deployment, integration support, and 3 months of runtime
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50% Production Discount
Locked pricing for 24 months when you go live
5 spots remaining • Applications reviewed within 3 business days
Supported Architectures
Deploys as a sidecar. Agnostic to your model, native to your stack.
AWS Lambda
Serverless
Kubernetes
Container Orchestration
Docker
Containers
LangChain
Agent Framework
OpenAI
LLM Provider
PostgreSQL
Database
Framework-agnostic architecture supports custom agents, APIs, and infrastructure