Agentic AI Testing
for Autonomous AI Systems.
Agentic AI — systems that plan, use tools, and take sequential actions — breaks every assumption traditional testing and LLM testing were built on. KiwiQA's agentic AI testing practice provides the validation framework these systems require before enterprise deployment.
Agentic AI systems can take irreversible
real-world actions. Standard QA cannot validate them.
An agentic AI that sends an email to the wrong recipient, executes a database delete, makes a financial transaction, or triggers an API in an unintended state has caused real-world harm that no traditional test suite would have caught — because the failure mode doesn't exist in deterministic software.
Six validation dimensions
for autonomous AI systems.
Every dimension of agentic AI behaviour requires a purpose-built test approach. Goal completion is not equivalent to step execution. Security is not equivalent to output safety. Our framework addresses each dimension explicitly.
Agentic AI systems
we test across every domain.
Agentic AI is being deployed across every business function. The failure modes differ by domain — but the need for structured validation is universal.
What our agentic AI testing
clients say.
We built an autonomous sales outreach agent that researches prospects, personalises emails, and schedules follow-ups. KiwiQA's agentic testing found it would occasionally send personalised emails using data from the wrong prospect profile when two names were similar. We also had a guardrail bypass where crafted prospect data could redirect the agent to ignore our messaging rules.
Expert guides on
agentic AI quality assurance.
Frequently asked questions
Everything you need to know — answered.
Agentic AI refers to systems that decompose goals into plans, take sequential actions using tools (APIs, search, code execution), and adapt based on intermediate results — unlike a single-turn LLM that responds and stops. This creates fundamentally different failure modes: irreversible actions, compounding errors across steps, and attack surfaces that don't exist in traditional software.
Goal completion testing evaluates whether an agent achieves its intended outcome at the end of a multi-step workflow — not whether each individual step ran without error. An agent can successfully execute every tool call and still fail the goal. We measure task completion rate, plan fidelity, goal drift across long-horizon tasks and recovery behaviour after partial failures.
We build a comprehensive tool use test suite covering: correct tool selection from available options, parameter binding accuracy, response interpretation correctness, error handling for API failures, retry behaviour under rate limiting, and misuse cases where ambiguous inputs could lead to wrong tool invocation. We test each tool in isolation and as part of end-to-end agent workflows.
Guardrails are the constraints that bound an agent's permitted action space — what it's allowed to do, which data it can access, which systems it can modify. We test guardrails using adversarial inputs designed to push agents outside their permitted scope, including crafted user instructions, injected content in retrieved documents, and multi-turn conversations that gradually expand apparent permission.
Multi-agent systems exhibit emergent behaviour not visible when each agent is tested in isolation — agent A's output becomes agent B's input in ways that create failure modes specific to the orchestration topology. We build integration test suites covering agent-to-agent message validation, error propagation, goal state consistency across agents and failure recovery in supervisor-worker and peer-to-peer topologies.
The primary novel risk is indirect prompt injection: malicious content embedded in documents, emails or web pages that an agent retrieves as part of its task. This content can hijack the agent's goal, redirect its tool use or cause it to exfiltrate data. Secondary risks include privilege escalation through tool chaining, where an agent combines multiple permitted tools to produce an effect no single tool permits.
Your agentic AI takes actions.
We validate they're the right ones.
Goal completion testing, tool use validation, guardrail testing, multi-agent orchestration and indirect prompt injection defence — purpose-built for autonomous AI systems that act in the real world.
covered in every engagement