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WHAT WE DO

AI Assurance: Independent Evidence. Confident Decisions.

AI systems can produce the right answer while making the wrong decisions along the way.

QASource independently evaluates AI and agentic systems beyond their outputs: the observable decisions they make, the tools they call, how they behave across a workflow, and how they hold up under security and reliability testing.  We combine automated evaluation with expert human judgment and give you traceable evidence of what works, what fails, and what risk remains, so you can decide what to ship. We never assess a system we built.

Why Independent

AI Assurance is a separate practice at QASource. We never assess an AI system that QASource designed, built, or helped build, and the assurance team takes no development or remediation work on any system it assesses. Before each engagement starts, we confirm there is no conflict and agree on the scope, evaluation criteria, ownership boundaries, and reporting lines in writing. We report failures, limitations, and residual risks as we find them, whether or not the system meets its acceptance criteria.

We work with CTOs, VPs of Engineering, and AI and risk leaders who need objective, evidence-based assurance findings and documented evaluation results to support executive decision-making, regulatory reviews, internal governance, and responsible AI deployment. Our deliverables, grounded in both automated evaluation and human validation, provide visibility into system behavior, evaluation coverage, observed failures, AI-specific security findings, corrective actions, and residual risks, so you can make, and defend, a release decision.

What We Validate

  • Behavior Under Realistic Use: Does the system do what it is supposed to across the distribution of inputs it will actually see, including the ones the product team never wrote down?
  • Failure Modes: Where it hallucinates, drifts, refuses, or acts outside its intended scope; how often; and what the consequence is when it does.
  • Guardrails and Policy: Whether the controls that are supposed to constrain the system hold under adversarial and accidental pressure.
  • Retrieval and Grounding: Whether the context retrieved is the right context and whether the answer is actually grounded in it rather than in the model's own priors.
  • Multi-Agent Handoffs: Where work passes from one agent to another, and what is lost in the handover: state, intent, or the constraint that was supposed to travel with it?
  • Adversarial Behavior: Deliberate attempts to make the system misbehave (prompt injection, jailbreaks, guardrail bypass), and which controls hold.
  • Tool Use and Agent Actions: For systems that act on behalf of people, whether the right tool is called with the right arguments and what happens when a call fails, when instructions conflict, or when the environment changes underneath.
  • Data Integrity: Whether the pipelines feeding the system produce consistent inputs, and what happens when they don't.
  • Regression Over Time: A baseline you can re-run when the model, the prompt, or the data changes, so "it worked in March" is a fact rather than a memory.

Judgment Is What's Being Tested

Modern AI evaluation combines automated behavioral assessment with expert human judgment. Automated evaluations assess outputs, decisions, tool selection, execution trajectories, task completion, policy compliance, and consistency against defined criteria. Expert human validation complements these assessments by evaluating subjective, contextual, domain-specific, and experience-driven aspects of AI behavior that automated evaluations cannot reliably judge on their own. Human-scored reference sets are also used to check how far automated evaluators and AI judges can be trusted. Together, these layers give a more complete and repeatable assessment than either provides alone.

Those are human assessments, and clients are now asking for them by name. QASource supplies trained validators who work through agents, workflows, and processes by hand, under a defined method.

How human validation works →

Engagement Model: From AI Assessment To Release Confidence

Our engagement follows a structured, evidence-driven approach to assess AI system reliability, identify risks, and support informed release decisions.

  • Understand the System: Establish the business objectives, system architecture, intended behavior, risk exposure, and evaluation criteria.
  • Define the Evaluation Approach: Develop risk-based test scenarios, representative datasets, evaluation rubrics, and adversarial test cases aligned with the system's intended use.
  • Assess AI Behavior and Decisions: Evaluate outputs, decision-making, tool usage, and execution workflows through automated assessments, expert human validation, trace analysis (where available), and repeated-run testing.
  • Investigate Findings and Validate Improvements: Identify and document failures with supporting evidence, assess their severity and likely causes, and verify corrective actions through targeted retesting and regression testing.
  • Deliver Release-Readiness Insights: Provide a consolidated assessment of findings, limitations, unresolved issues, and residual risks, along with recommendations to support release decisions and ongoing monitoring.

What You Receive

  • A written assurance finding: scope, method, results, failure modes observed, residual risk, and what would change our conclusion
  • Human validation results where judgment was assessed: rubric, scores, rater agreement, and the disagreements themselves
  • The evaluation harness itself, so your team can re-run it
  • Ground-truth datasets built for your system, owned by you
  • Bounded runtime monitoring where the system's behavior can change after release

Each Entry Traces a Failure to Where It Started

This is one entry from a written assurance finding. Your finding has one entry for each failure mode we confirm.

Illustrative sample

Finding AA-07

Retrieval and Grounding; Guardrails and Policy

High severity

System

Document question-and-answer assistant built on retrieval-augmented generation (RAG).

Scenario

A user who can open only the HR policy documents asks who approved the last exception to the travel policy.

Expected

The answer comes only from documents this user can open. When those documents don't hold the answer, the assistant says so.

Observed

The answer named the employee who was granted the exception and quoted a line from their HR case file, which this user can’t open. Its citation pointed to the travel policy, which doesn’t contain that line.

Evidence

Transcript, retrieved passages with source IDs, the user's permission set, and repeat-run logs.

Confidence

High. The same question from the same user returned the restricted line on repeat runs.

Where It Started

Retrieval searched the full index. The permission check ran on the cited source after the answer was written, so the restricted passage got through.

Recommended Fix

Apply the user's permissions at retrieval, before ranking. Check each claim in the answer against the passage it cites.

Our Recommendation

Release to users with restricted document sets after the retest passes. The release call is yours.

Retest

After the fix, we re-ran this scenario with the related cross-document scenarios. Restricted content didn't appear in the retest answers.

Residual Risk

Permission changes made during a live session weren't in scope. We list them as untested.

What Would Change This Entry

A new document source or a change to how permissions are applied reopens it.

Illustrative: built to show our format, not taken from a client engagement.

Your team can re-run this entry from the evidence without a call to us.

Case Studies

Case study

Quality Validation of an Agentic AI-Powered QA Platform

The Challenge

The AI-powered QA platform under assessment used multiple agents to interpret software requirements and generate test scenarios and test cases.

QASource conducted an independent quality assessment to evaluate not only the generated QA deliverables but also how agents interpreted requirements, made decisions, selected tools, coordinated tasks, retained context, and handled failures throughout the workflow.

Assessment Scope

The assessment covered:

  • Requirement Interpretation: Accuracy in identifying requirements, constraints, dependencies, and expected test coverage.
  • Agent Decision-Making: Quality of intermediate decisions and task decomposition.
  • Tool Selection and Usage: Appropriate tool selection, invocation, parameter handling, and response interpretation.
  • Workflow Orchestration: Agent sequencing, task delegation, dependencies, and execution trajectories.
  • Inter-Agent Communication: Accuracy and completeness of information exchanged between agents.
  • Context Integrity: Retention of requirements, constraints, and decisions across agent handoffs.
  • Repeated-Run Reliability: Consistency of agent decisions, execution paths, and generated QA artifacts across multiple runs.

Evaluation Methodology

The assessment examined both final outputs and intermediate agent behavior. Where trace and span data was available, individual execution steps were reviewed to identify the origin of failures.

Testing focused on:

  • Requirement and Artifact Validation: Evaluating requirement coverage, test scenario quality, test case accuracy, completeness, and traceability.
  • Agent Planning and Decision Validation: Examining task decomposition, intermediate decisions, and alignment with intended objectives.
  • Tool and Execution Validation: Verifying tool selection, invocation, execution order, and handling of unsuccessful actions.
  • Context and Handoff Validation: Checking whether requirements, constraints, and agent outputs were accurately retained across workflow stages.
  • Failure and Recovery Testing: Evaluating responses to missing information, conflicting instructions, invalid outputs, and interrupted workflows.
  • Repeated-Run Evaluation: Comparing decisions, execution trajectories, and generated artifacts across repeated executions.

Evidence And Assessment Deliverables

Each finding documented the original requirement, expected and actual behavior, supporting evidence, severity, potential impact, affected agent or workflow stage, and recommended corrective action.

Where available, evidence included execution traces, spans, tool calls, intermediate agent outputs, and generated test artifacts. Findings also included retest status, distinguishing verified issues from potential risks and limitations where execution visibility was unavailable.

Corrective Actions And Verified Improvements

Recommendations were shared with the development team to improve requirement interpretation, agent decision-making, task decomposition, tool usage, inter-agent communication, context retention, workflow orchestration, and failure recovery.

Following the implementation of corrective actions over a couple of sprint cycles, final testing results demonstrated the following improvements:

Evaluation Area Initial Result Post-Fix Result
Overall workflow task success rate 72% 90%
Requirement interpretation accuracy 82% 94%
Agent decision accuracy 74% 91%
Task decomposition accuracy 76% 92%
Tool selection and invocation success 80% 95%
Context retention across agent handoffs 80% 94%
Workflow execution completion rate 78% 92%
Inter-agent communication accuracy 77% 91%
Failure recovery success rate 65% 85%
Repeated-run outcome consistency 76% 92%
Generated test case completeness 79% 93%

Previously failing scenarios were individually retested, including regression checks across other agents and workflow stages. Remaining failures were documented for further investigation.

Outcome: Assessing Agent Reliability Beyond Final Outputs

The engagement provided an independent, QA-focused assessment of both agent behavior and generated QA deliverables.

By examining agent decisions, execution trajectories, tool usage, context integrity, orchestration and recovery mechanisms, QASource helped identify the stages responsible for failures and evaluate the effectiveness of corrective actions.

The assessment established a structured approach to evaluating the reliability, consistency and readiness of agentic AI workflows for real-world QA activities.

What We Don't Claim

We do not certify that an AI system is safe. No one honestly can. We state what we tested, what we observed, and how confident that makes us, within bounds, we write down.

We do not audit for fairness or bias. That is a distinct discipline, and we will tell you who does it well.

We do not validate systems we built.

Bring Us The System You're Least Sure About

A scoped assurance assessment takes three to four weeks and produces a finding you can act on.

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