TestZeus vs Hamming vs Cekura: What Are You Actually Testing?

An AI agent can give the right answer and still fail the test.

That sounds contradictory until you look at what the agent is actually doing.

It may return the correct order status but never retrieve the underlying record. It may say an update was completed when the tool call never happened. It may handle a clean conversation perfectly and then fall apart when a caller interrupts, changes language, or starts speaking through background noise.

That is why comparing AI agent testing platforms by feature count alone misses the bigger question:

What does the platform actually help you prove?

TestZeus Agent Harness, Hamming, and Cekura all address AI agent testing, but they approach the problem from different angles. TestZeus goes deep into Salesforce and Agentforce. Hamming puts strong emphasis on voice-agent testing at scale and production monitoring. Cekura puts significant depth into multilingual and realistic voice simulation.

The right comparison starts there.

Agent testing is no longer just a conversation test

Traditional software testing often starts with a fairly predictable relationship between input and output.

Agents complicate that model.

An agent can interpret language, maintain context across multiple turns, choose actions, call tools, retrieve information, and affect external systems.

So a useful agent test needs to ask more than:

“Did the response sound right?”

It also needs to ask:

“Did the agent do the right thing?”

That distinction is where these platforms start to separate.

TestZeus Agent Harness: testing the agent and the system behind it

TestZeus is built for teams testing Salesforce applications and Agentforce agents together.

Its Agent Harness supports Agentforce testing, multi-turn conversations, end-to-end workflows, generated test pathways, and custom scenarios. More importantly, it extends testing beyond the conversation itself into the Salesforce environment where the agent operates.

Consider a simple example.

An Agentforce agent tells a customer:

“Your shipping date has been updated.”

A conversational test can determine whether that response is plausible.

A deeper test asks whether the Salesforce record actually changed.

That is where TestZeus takes a different approach. Its testing can use SOQL to validate Salesforce state against what the agent claimed, turning the test from a response check into an outcome check.

And that distinction matters because enterprise agents do not operate in isolation. They interact with records, Flows, Apex, APIs, UI workflows, and other systems.

So the test oracle cannot always be the transcript.

Sometimes, the real oracle is the system of record.

That same broader Salesforce testing scope extends beyond agents into Apex, LWC, Flows, UI, and API testing.

The TestZeus view is straightforward:

An agent should not be considered trustworthy just because the conversation looks good. The underlying business outcome needs to be testable too.

Hamming: where voice testing meets scale and production

Hamming approaches the category from a strong voice and conversational-AI perspective.

Its platform covers voice and chat agents, automated scenario generation, red teaming, large-scale testing, and production monitoring. Its publicly advertised capabilities include 50K+ concurrent test calls and 50+ built-in metrics.

That changes the testing question.

Instead of manually checking whether one call works, teams can evaluate how an agent behaves across large numbers of conversations and under changing voice conditions.

Hamming emphasizes accents, interruptions, background noise, latency, task completion, and production-call monitoring.

For teams where voice reliability at scale is central, that matters.

The question becomes less:

“Can this agent handle a call?”

and more:

“Can this agent keep handling calls when the volume, conditions, and user behaviour change?”

That is a different testing problem.

Cekura: realism, language, and conversational conditions

Cekura also focuses heavily on voice and conversational AI, with an emphasis on simulation depth and the variety of conditions in which an agent can be evaluated.

Its current materials describe testing across 32 languages and 31 background environments, with controls for accents, emotions, speaking speed, volume, interruptions, and other conversational characteristics.

That matters because real conversations are messy.

A customer may switch languages halfway through a call. Someone may speak faster than expected. There may be traffic or other noise in the background. A caller may repeatedly interrupt the agent.

A clean scripted call does not expose those behaviours.

Cekura also supports production observability and enterprise deployment options, including customer-cloud deployment and regional data residency.

Its underlying proposition is simple:

The closer your test conditions are to real conversations, the more useful the evidence becomes.

TestZeus vs Hamming vs Cekura: the differences at a glance

The three platforms overlap, but their centre of gravity is different.

The key takeaway is not that one column is universally better than another.

It is that different platforms answer different testing questions.

Don't compare the demo. Compare the evidence.

A polished AI-agent demo can look impressive in five minutes.

That is not the same thing as proving the agent is ready for production.

A better evaluation is to give every platform the same scenarios and compare the evidence each one produces. 

Start with five questions.

1. Did the business outcome actually happen?

Ask the agent to perform an action.

Then verify whether the underlying system changed.

2. Does the answer match the source of truth?

Ask for information that exists in a system of record.

Then check the response against that source.

3. What happens when the user behaves badly?

Test prompt injection, sensitive-data requests, unexpected inputs, and attempts to bypass guardrails.

4. Does the agent survive real conversation conditions?

For voice agents, introduce interruptions, accents, background noise, language changes, pauses, and latency.

5. Can the team understand the failure?

When a three-minute conversation fails, can the tester get to the failure point quickly, reproduce it, and understand what needs to change?

That last question is easy to underestimate.

A testing platform is only useful when its output helps the team make a better release decision.

A practical AI agent testing scorecard

Before comparing platforms, define what “passing” actually means for your agent.

A useful scorecard should measure more than conversational quality:

Outcome accuracy- Did the requested action actually happen?

Ground-truth accuracy- Did the response match the underlying data?

Safety- Did the agent protect sensitive information and stay within its boundaries?

Voice behaviour-  How did it respond to interruptions, noise, accents, and latency?

Scale-  Can the testing strategy handle the volume your deployment requires?

Debuggability-  When a test fails, can the team quickly understand why?

Evidence-  Does the platform produce enough detail to support a real release decision?

The point is not to create a bigger checklist.

It is to stop treating “passed” as a sufficient answer.

The real comparison is evidence, not the feature list

There is no single dimension that defines a good agent-testing platform.

A Salesforce team may care deeply about whether an agent's claims match Salesforce records. A voice-first team may care more about concurrency, interruption handling, or production-call monitoring. A global deployment may put multilingual and regional voice conditions at the top of the list.

That is why the better question is not:

“Which tool has more features?”

It is:

“Which tool gives us evidence about the risks we actually need to control?”

For TestZeus, that means going beyond the conversation and into the enterprise systems the agent is operating against.

An agent can sound confident.

It can be fluent.

It can pass thousands of conversational checks.

But when that agent changes a customer record, triggers a Flow, retrieves business data, or takes an action on someone's behalf, the test has to follow it there.

That is the shift from testing what an agent says to testing what an agent actually does.

The TestZeus perspective

At TestZeus, we think agent testing is moving from script maintenance to agent supervision.

The goal isn't to create another pile of test cases.

The goal is to continuously generate credible evidence that an agent is behaving correctly across conversations, business rules, data, tools, and real enterprise workflows.

For Salesforce and Agentforce teams, that means testing the agent in the same environment where its decisions have consequences—and validating those consequences against the underlying system of record.

That is the problem TestZeus Agent Harness is built around.

Don't just test what your agent says. Test what it does.

Want to test what your AI agent actually does?

Explore TestZeus Agent Harness and see how AI-agent testing can extend from the conversation to the workflow, data, and business outcome.