Multi-Agent Testing

End-to-end testing for multi-agent orchestration.

We validate the full agentic loop — tool use accuracy, handoff integrity, memory consistency, goal completion, and graceful failure recovery — built for LangGraph, CrewAI, AutoGen, and custom orchestration layers.

Full

Agent Graph Coverage

Per-tool

Accuracy Testing

Per-step

Memory Consistency

5

Failure Scenario Classes

Why this matters

Multi-agent systems fail in ways that single-model evals can't detect. A handoff that loses context, a tool called with the wrong parameters, a memory that contradicts itself three steps later — these failures are invisible until a workflow completes wrong. By then, the damage is done.

How We Do It

A structured process, every engagement.

01

Map your agent graph

We document your full orchestration topology — agents, tools, handoff paths, memory stores, and failure modes.

02

Test tool use accuracy

Every tool the agents can call is tested for correct selection, parameter passing, and output handling across diverse scenarios.

03

Test handoff integrity

Context preservation across agent boundaries — does the receiving agent have everything it needs, and nothing it shouldn't?

04

Test memory consistency

Information introduced early in a workflow is checked for accuracy and consistency across subsequent agent steps.

05

Simulate failure scenarios

Tool failures, timeout loops, unexpected outputs, and goal divergence — what does your system do when things go wrong?

What You Get

Tangible deliverables, not slide decks.

Agent graph documentation and orchestration map
Tool use accuracy report per tool
Handoff integrity analysis across agent boundaries
Memory consistency test results
Failure mode documentation with severity ratings
Prioritised remediation recommendations

Who It's For

Built for teams where AI reliability is non-negotiable.

Multi-agent orchestration teams

Teams running LangGraph, CrewAI, AutoGen, or custom orchestration layers where a single agent failure cascades.

Tool-using AI assistants

Agents that call external APIs, databases, or services — where wrong tool use causes real downstream harm.

High-stakes autonomous workflows

Agents making decisions or taking actions without real-time human review — where silent failures are the highest risk.

Ready to get started?

Book a free 30-minute AI Reliability Assessment. We'll review your stack, identify your highest-risk failure modes, and show you exactly what to fix first.

Book Your Free Assessment →