Multi-Agent Battle-Testing Platform for AI Agents

What it is

A quality and performance testing platform that stress-tests AI agents using multi-agent simulations before deployment - like a professional "test bench" for agents.

Instead of "try it a few times and ship," it produces objective metrics and reliability scores.

What it solves

AI agents fail in production because:

• They behave inconsistently across users
• They hallucinate or invent facts
• They get stuck in loops or waste steps
• They break under edge cases and adversarial prompts
• They become expensive and slow unpredictably

This platform solves: standardized evaluation + reliability proof + measurable productivity.

How it works

1. Define a target agent

• The user plugs in their agent (any framework / any runtime conceptually)
• The platform treats it like a black box "agent under test"

2. Run simulated environments and personas

Multiple tester agents simulate different user types:

• Normal user
• Impatient user
• Power user
• Confused user

Specialized adversary agents try to break it:

• Prompt injection attempts
• Policy bypass attempts
• Misleading instructions

3. Evaluator agents score the outcomes

Separate evaluator agents judge results:

• Correctness and completeness
• Hallucination detection signals
• Safety violations
• Consistency across runs

4. Generate benchmark reports

The platform generates:

• A scorecard (reliability, productivity, safety)
• Failure map (where it breaks)
• Comparisons vs previous versions
• "Fix recommendations"

Key features

Multi-agent simulation

• Persona simulations (user types)
• Adversary simulations (attackers)
• QA simulations (structured test flows)

Productivity metrics

• Task completion rate
• Steps required to finish tasks (efficiency)
• Consistency between runs (stability)
• Cost efficiency measures (how "wasteful" the agent is)

Quality + reliability metrics

• Hallucination indicators (claims unsupported / inconsistent)
• Robustness to ambiguous prompts
• Failure recovery behavior (does it self-correct or collapse?)

Safety + compliance metrics

• Policy boundary adherence
• Data leakage attempts
• Injection resistance scoring

Regression tracking

• Compare version A vs version B
• Identify "what got worse" after a change
• Release gates: do not ship if key metrics drop

Reporting + shareability

• Dashboard view for teams
• Exportable PDF reports
• Shareable links for stakeholders (product, compliance, clients)

Primary audiences

• Teams building AI agents for real products
• Agencies delivering agent-based solutions to clients
• Enterprises that need proof of reliability before adoption
• Any team worried about safety, cost unpredictability, and failures

Differentiator

Most evaluation tools test responses.

This system tests agent behavior over time using multiple simulated agents, producing battle-grade reliability metrics.