Simulate Your AI Agents.
Before They Eat Your Budget.
Build multi-agent AI systems on a live canvas. Wire RAG pipelines, orchestrators, and guardrails. Then hit them with token explosions, hallucination cascades, and prompt injection — before any of this reaches production.
What You Can Simulate
Every major AI system pattern — wired, routed, and stressed under realistic load.
RAG Pipelines
Wire retrieval, chunking, embedding, and generation. Tune chunk size and observe relevance score changes in live traffic.
Multi-Agent Orchestration
Planner → Executor → Critic patterns. Watch agents hand off tasks, retry on failure, and escalate to humans.
A2A Gateways
Agent-to-agent message passing with typed payloads. Simulate fanout, aggregation, and conditional branching.
Guardrails
Place content filters, PII detectors, and output validators. Verify they actually intercept malicious outputs.
Prompt Injection Attacks
Fire adversarial prompts at your pipeline. See which layers catch them and which let them through.
Confidence Routing
Route low-confidence outputs to fallback models or human review. Balance cost vs. accuracy dynamically.
AI-Specific Scoring
How reliably does your pipeline produce correct outputs under load?
P50/P99 response times across agent hops. Identify the slow node.
Token spend per query. Catch token explosion before your bill does.
Guardrail coverage rate. What percentage of harmful outputs are blocked?
Variance in outputs across demographic prompt variations.
Can you trace every decision back to a source? Chain of thought completeness.
AI Failure Scenarios
An agent loops without a termination condition. Token spend spikes. Detect and add a budget governor before the bill arrives.
Model confidence falls below threshold on ambiguous queries. Is your routing logic sending them to the right fallback?
Malicious input hijacks your agent's instructions. Does your sanitizer sit upstream of every LLM node?
One agent fabricates a fact. Downstream agents use it as ground truth. Implement a validator to break the chain.
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