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.

35
AI Models
9
Agent Components
4
Routing Strategies
$0
Real Spend to Practice

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

Accuracy

How reliably does your pipeline produce correct outputs under load?

Latency

P50/P99 response times across agent hops. Identify the slow node.

Cost

Token spend per query. Catch token explosion before your bill does.

Safety

Guardrail coverage rate. What percentage of harmful outputs are blocked?

Bias

Variance in outputs across demographic prompt variations.

Explainability

Can you trace every decision back to a source? Chain of thought completeness.

AI Failure Scenarios

Token BurnCritical

An agent loops without a termination condition. Token spend spikes. Detect and add a budget governor before the bill arrives.

Confidence DropHigh

Model confidence falls below threshold on ambiguous queries. Is your routing logic sending them to the right fallback?

Prompt InjectionCritical

Malicious input hijacks your agent's instructions. Does your sanitizer sit upstream of every LLM node?

Hallucination CascadeHigh

One agent fabricates a fact. Downstream agents use it as ground truth. Implement a validator to break the chain.