Adversarial pre-deployment gating for autonomous agents.
Catch recursive retry loops, destructive tool calls, prompt injections, and runaway token costs before merging to production.
The 50-Test Initial Adversarial Battery
Standardized failure vectors targeting the 5 breakdown modes of autonomous multi-step agents in production.
Infinite Loops & Error Cascades
Agent enters recursive retry loops upon encountering mock 500 API errors, malformed JSON schemas, or dead-end search trees.
Halts after ≤ 3 retries, triggers deterministic fallback, consumes ≤ 1.5× base token budget.
Built for Founders & AI Platform Leaders
Autonomous agents operate without human intervention. AuditGPT ensures reliability, data protection, and unit economics before features merge.
Zero Runaway Inference Bills
Prevent background agent retry loops and runaway token blowouts from turning a $50/mo staging bill into a $12,000 billing emergency.
Immunity to Data & Prompt Leaks
Ensure multi-turn prompt injections, role-play jailbreaks, and memory scanning cannot exfiltrate internal system prompts or API keys.
Automated Merge Gate Compliance
Empower engineering teams to ship agent features rapidly without worrying that a prompt tweak broke multi-turn constraints in production.
Zero-Dependency CLI & GitHub Action Defense Gate
Run ASTI v1.1 locally in one command. Block breaking prompt and tool changes in your CI pipeline.
# yaml-language-server: $schema=https://auditgpt.ai/auditgpt.schema.json
version: "1.1"
target:
endpoint: "http://localhost:8000/agent/v1/chat"
protocol: "openai" # Options: openai | anthropic | rest | custom_webhook
model: "gpt-4o-mini"
headers:
Authorization: "Bearer ${AGENT_API_KEY}"
timeout_ms: 30000
testing:
suites: ["loops", "tools", "state", "security", "economics"]
concurrency: 5
runs_per_test: 1
bounds:
max_cost_per_task_usd: 0.15
max_tokens_per_task: 4000
max_total_budget_usd: 5.00
gates:
pass_threshold: 80.0
fail_on_injection: true
fail_on_unauthorized_tool: true
fail_on_infinite_loop: true
min_deterministic_parity: 75.0
reporting:
formats: ["terminal", "markdown", "sarif"]
export_pr_comment: "audit_summary.md"
export_sarif: "audit_results.sarif"
ci_mode: trueContinuous Runtime Anomaly & Drift Monitoring
Pre-deployment gating stops bad PRs. The runtime layer monitors production traces for live context drift, recursive retry loops, and sudden cost spikes.
Cost-per-Task Anomaly Alerts
Detects when background agent retries drive effective task costs above your defined threshold ($0.15), alerting via Slack or PagerDuty before monthly bills explode.
Production Loop Circuit Breaker
Monitors active multi-agent orchestrations and automatically terminates recursive execution cycles when an agent repeats duplicate tool calls $\ge 3$ times.
OTel & Trace Native
Zero vendor lock-in. Stream agent traces via standard OpenTelemetry (OTLP), Langfuse, LiteLLM, or custom webhooks with zero measurable latency overhead.
Why Generic Eval & Tracing Tools Fall Short
Traditional prompt diffing and passive trace logging were built for single-turn chatbots. Autonomous multi-step agents require adversarial chaos gating.
| Dimension | TestMu Assurance | Promptfoo / DeepEval | LangSmith / Langfuse | AuditGPT.ai |
|---|---|---|---|---|
| Primary Focus | Manual policy forms & slow UI checks | Static prompt diffs & red-teaming | Passive production tracing & logs | Adversarial chaos & economic gating for agents |
| Developer Friction | Heavy dashboard setup & sales demos | Local CLI config | SDK tracing integration | Zero-config local CLI (npx auditgpt test) |
| Failure Detection | Abstract policy violations | Semantic similarity & basic injection | Post-hoc latency & cost charts | Infinite retry loops, tool cascades, token cliffs |
| CI/CD Action | Non-blocking dashboard reports | Matrix diff comparisons | None (dashboard-only telemetry) | Hard PR merge gates, SARIF exports, exit codes |
| Economic Defense | None | None | Retrospective billing charts | Hard cost-per-task ceilings & loop circuit breakers |
vs. TestMu Agent Assurance
Architectural Flaw: Extension of traditional QA testing with high manual overhead, slow test cycles, and no bottom-up developer velocity.
vs. LangSmith / Langfuse / Helicone
Architectural Flaw: Passive telemetry records disasters after they occur in production, after the customer sees broken outputs, and after the token bill explodes.
vs. Promptfoo & DeepEval
Architectural Flaw: Built for single-turn prompt diffing and RAG semantic benchmarks. Zero specialized harnesses for multi-turn agent state machines or tool dependency loops.
Predictable Chaos Testing for Engineering Teams
Open-source CLI for individual builders. Automated PR merge gates and runtime anomaly alerting for engineering organizations.
Full 50-test ASTI battery for local development and debugging.
- All 50 adversarial test vectors
- Local execution (`npx auditgpt test`)
- Markdown & SARIF trace exports
- Community Discord & GitHub support
For engineering teams deploying autonomous agents to production.
- GitHub Action PR blocking defense gate
- 5 engineering team seats included
- Runtime anomaly alerts (100k calls/mo)
- Automated PR failure trace diff summaries
For high-volume multi-agent systems and enterprise compliance.
- Custom domain adversarial test scenarios
- Multi-agent state & trace replay debugger
- Real-time Slack & Teams incident alerts
- Enterprise SSO, SAML & dedicated VPC
Frequently Asked Questions
Stress-test before production.
Run the open-source CLI locally or add the GitHub Action gate to block runaway loops and prompt injections before merge.