Scaling AI Across the Portfolio Without Scaling Risk

How SureWire™ Helps Private Equity Firms and Portfolio Companies Deploy AI Agents with Confidence

Executive Summary

Private equity firms are increasingly encouraging, and in some cases requiring, their portfolio companies to adopt AI across the enterprise. The opportunity is significant. AI agents and workflows can accelerate software development, improve go-to-market execution, increase marketing productivity, streamline administrative work, and help teams operate with greater speed and efficiency.

But as AI moves from experimentation to production, the risk profile changes.

AI agents are not traditional software applications. They are non-deterministic systems that interpret instructions, generate outputs, retrieve information, interact with tools, and sometimes make or recommend decisions. This flexibility is what makes them powerful. It is also what makes them difficult to validate using conventional quality assurance methods.

A workflow that “seemed to work” during a demo may behave differently when exposed to real users, new data, adversarial prompts, edge cases, or a model update. It may hallucinate, leak sensitive information, ignore policy boundaries, produce inconsistent outputs, or take actions that were never intended.

For private equity firms, this creates a new challenge: how to drive AI adoption across the portfolio while protecting enterprise value, customer trust, compliance posture, and operational resilience.

SureWire™ was created to address this gap. SureWire is a QA platform built specifically for AI agents, designed to test for safety, consistency, compliance, reliability, and behavioral risk before agents are deployed into real-world business environments. SureWire is the first QA platform built specifically for AI agents, focused on testing AI systems before they meet the real world.

For private equity firms and their portfolio companies, SureWire provides a practical way to move from AI experimentation to AI assurance.

The AI Mandate Is Reaching Every Function

AI is no longer confined to innovation labs or isolated productivity pilots. Across PE-backed companies, AI is being embedded into daily workflows, including:

• Software development and testing

• Sales prospecting and account research

• Marketing content generation and campaign optimization

• Customer support and success operations

• Finance, HR, legal, procurement, and administrative workflows

• Internal knowledge management and employee self-service

• Data analysis, reporting, and decision support

The appeal is clear. AI can reduce manual work, compress cycle times, increase productivity, and allow smaller teams to operate with greater leverage. For PE firms focused on operational improvement, margin expansion, and scalable value creation, AI is becoming a powerful part of the operating playbook.

However, the same attributes that make AI valuable also make it risky. AI agents do not simply execute predefined logic. They generate responses, interpret context, and may interact with systems, documents, APIs, or business tools. As a result, traditional software controls are not always enough.

The question is no longer whether portfolio companies should use AI.

The more important question is:

How can organizations prove that their AI agents are safe, reliable, and ready for production?

Why AI Agents Introduce a New Class of Enterprise Risk

Traditional software is usually tested against expected inputs and outputs. If a user clicks a button, submits a form, or calls an API, the system should respond in a predictable way. AI agents are different.

They are probabilistic. They may respond differently to similar prompts. They may interpret ambiguous instructions in unexpected ways. They may rely on external context, retrieved documents, tool calls, model behavior, or system prompts that change over time.

This creates several major risk categories for PE-backed companies:

1. Confidently Wrong Answers

AI systems can generate plausible but incorrect responses. In low-risk settings, this may be inconvenient. In business-critical workflows, it can be costly.

• A sales assistant might invent a product capability.

• A customer support agent might misstate a refund policy.

• A coding agent might recommend an insecure implementation.

• An HR assistant might provide incorrect guidance on an internal policy.

• A compliance workflow might summarize a regulation inaccurately.

These failures are especially dangerous because AI systems often sound authoritative even when they are wrong.

2. Data Leakage

AI agents may interact with sensitive information such as customer records, contracts, source code, financial data, employee records, health information, or proprietary business plans. If not properly tested, an agent may expose information to the wrong user, include confidential data in generated outputs, or retrieve documents outside the intended scope.

For portfolio companies in healthcare, life sciences, financial services, government contracting, insurance, manufacturing, or enterprise software, this is a material business risk.

3. Prompt Injection and Adversarial Inputs

AI agents can be manipulated through malicious or unexpected inputs. A user may attempt to bypass system instructions, override policies, extract confidential information, or force the agent to behave outside its intended boundaries.

Prompt injection and adversarial inputs have been identified as key risks that AI agents face in production environments. These risks become more serious when an AI agent has access to business systems, internal documents, customer data, or workflow automation tools.

4. Behavioral Drift

AI behavior can change over time. A model update, prompt adjustment, knowledge-base change, tool integration, or new data source can alter how an agent responds. Even if an AI workflow worked correctly during initial testing, it may behave differently later.

SureWire is designed to help teams detect this type of behavioral drift, especially when changes to models, prompts, or context affect agent behavior.

5. Lack of Auditability

In many organizations, AI adoption is happening faster than governance. Teams may be using AI tools without a clear record of what was tested, what risks were identified, what failures occurred, or what remediation steps were taken.

For PE firms, boards, auditors, compliance leaders, and future acquirers, this creates a documentation gap. Organizations need more than confidence. They need evidence.

Why Traditional QA Is Not Enough

Traditional QA remains essential for software applications, APIs, integrations, and user interfaces. But AI agents require a different testing approach.

A conventional test may ask:
Did the system return the expected result?

AI agent testing must ask broader questions:

• Did the agent provide a correct and useful answer?

• Did it follow the relevant policy?

• Did it refuse unsafe or inappropriate requests?

• Did it expose sensitive data?

• Did it behave consistently across similar scenarios?

• Did it handle ambiguity correctly?

• Did it escalate when it should?

• Did its behavior change after a model, prompt, or data update?

Pass/fail testing alone is not sufficient for systems that reason, improvise, and generate probabilistic outputs. SureWire was designed to address this exact challenge: conventional QA tools were built for deterministic software, while AI agents require testing for safety, consistency, compliance, and real-world failure modes.

For PE-backed organizations, this distinction matters. AI adoption cannot be governed solely through employee training, vendor questionnaires, or policy documents. Those controls are useful, but they do not prove that an AI agent behaves correctly under pressure.

Organizations need a way to actively test AI agents before they are exposed to customers, employees, sensitive data, or business-critical workflows.

Introducing SureWire™

SureWire™ is our AI-native QA platform for testing AI agents. It is designed to help organizations evaluate whether their agents are safe, reliable, consistent, compliant, and ready for real-world use.

At a high level, SureWire enables teams to:

• Describe the AI agent and the concerns around itTeams define what the agent does, how it is expected to behave, and what risks need to be evaluated.

• Dynamically assess and test the AI agentSureWire evaluates the agent against realistic scenarios, edge cases, adversarial inputs, and behavioral risks.

• Generate clear, actionable reportsTeams receive practical insights into the agent’s quality, risks, and improvement opportunities.

SureWire uses specialized testing agents that can act like hostile hackers, confused customers, and other real-world personas to evaluate AI systems more thoroughly. This approach helps organizations move beyond static test cases and toward dynamic, scenario-based AI assurance.

Why This Matters for Private Equity

Private equity firms are in a unique position. They often have the authority, urgency, and operating discipline to drive AI adoption across multiple companies. But they also have to manage risk across a diverse portfolio of businesses, industries, regulatory environments, and technology maturity levels.

A portfolio-wide AI strategy creates tremendous upside, but it also creates a need for consistency.

Without a common assurance model, each portfolio company may approach AI risk differently. One company may have mature governance and testing practices. Another may be deploying AI workflows informally through individual teams. A third may be using AI in customer-facing or regulated workflows without sufficient validation.

SureWire helps PE firms and portfolio companies introduce a more repeatable model for AI assurance.

It allows organizations to ask:

• Which AI agents are being used?

• Which workflows create the most risk?

• Have those agents been tested?

• What failure modes were found?

• Were issues remediated?

• Has the agent been retested after changes?

• Is there evidence that the agent is fit for purpose?

This turns AI assurance from an abstract governance discussion into a practical quality process.

Key Use Cases Across the Portfolio

Software Development and Engineering

AI coding assistants and development agents can accelerate software delivery, but they can also introduce hidden defects, insecure code, poor design choices, or hallucinated dependencies.

SureWire can help evaluate AI engineering workflows for:

• Secure coding behavior

• Consistency of generated outputs

• Quality of generated test cases

• Handling of ambiguous requirements

• Adherence to internal engineering standards

• Risky recommendations or unsupported assumptions

For PE firms pushing portfolio companies to modernize engineering productivity, this is critical. AI can help teams build faster, but quality and security cannot be left to chance.

Sales and Go-to-Market

AI is increasingly being used for prospecting, account research, outbound personalization, sales enablement, proposal drafting, and RFP responses. These workflows can improve sales productivity, but they can also introduce risk.

An AI sales assistant may generate unsupported claims, misrepresent product capabilities, misuse customer information, or produce messaging that conflicts with legal or brand guidelines.

SureWire can help test GTM agents for:

• Accuracy of product claims

• Consistency with approved messaging

• Handling of competitive comparisons

• Confidentiality and data-boundary issues

• Escalation of legal, pricing, or contractual questions

• Rejection of inappropriate or high-risk prompts

For PE-backed companies, GTM AI must not only be efficient. It must be trustworthy.

Marketing

Marketing teams are using AI to create content, analyze campaigns, generate SEO briefs, produce social media copy, personalize messaging, and summarize market research. These workflows can dramatically improve output, but they also require guardrails.

SureWire can help evaluate whether marketing agents:

• Stay within brand guidelines

• Avoid unsupported claims

• Respect regulated-industry language constraints

• Avoid confidential or proprietary disclosures

• Maintain consistency across campaigns and channels

• Produce content aligned with approved messaging

This is particularly important when portfolio companies operate in complex B2B, healthcare, financial, technical, or regulated markets.

Customer Support and Success

Customer-facing AI agents are often among the highest-value and highest-risk AI deployments. They can reduce support costs, improve response times, and scale knowledge across the customer base. But they also directly affect customer trust.

A support agent that provides incorrect instructions, discloses customer data, mishandles an angry customer, or fails to escalate a serious issue can create real business consequences.

SureWire can help test support agents for:

• Policy adherence

• Correct escalation behavior

• Consistent responses to similar issues

• Protection of sensitive customer information

• Safe handling of adversarial or frustrated users

• Accuracy across product, billing, and account scenarios

Before an AI support agent interacts with customers, companies should be able to prove that it has been tested against realistic and difficult scenarios.

Finance, HR, Legal, and Administration

Back-office AI workflows often touch sensitive internal data. Examples include invoice processing, contract review, employee policy assistants, procurement workflows, internal knowledge bots, and board-reporting support.

SureWire can help evaluate these agents for:

• Data access boundaries

• Policy accuracy

• Privacy protection

• Appropriate refusal behavior

• Escalation of sensitive requests

• Consistency across repeated scenarios

These workflows may not always be customer-facing, but they can still create significant compliance, privacy, and operational risk.

A Practical AI Assurance Model for PE Firms and Portfolio Companies

SureWire can support a practical, repeatable model for AI assurance across the portfolio.

Step 1: Identify AI Workflows

Portfolio companies should maintain an inventory of AI agents and workflows, including:

• Business function

• Use case owner

• Data sources used

• Users affected

• Customer-facing or internal status

• Tools or systems connected

• Level of autonomy

• Regulatory or contractual exposure

This does not need to be overly bureaucratic. The goal is visibility.

Step 2: Classify Risk

Not every AI workflow requires the same level of testing. A brainstorming assistant is different from a customer support agent or a workflow that handles regulated data.

Organizations can classify AI use cases by risk level:

Risk Level	Example AI Workflow	Assurance Need
Low	Internal idea generation	Basic review and periodic validation
Medium	Marketing or sales content assistant	Accuracy, brand, and policy testing
High	Customer support or engineering agent	Scenario testing, adversarial testing, data-boundary validation
Critical	Regulated, customer-impacting, or autonomous workflow	Deep testing, documented evidence, recurring validation

Step 3: Test Before Production

Before an AI agent is deployed into a live environment, SureWire can be used to test how it behaves under realistic conditions. This includes normal use cases, edge cases, ambiguous inputs, adversarial prompts, and safety-sensitive scenarios.

Step 4: Remediate and Retest

The purpose of AI testing is not to stop innovation. It is to identify weaknesses early so teams can improve the agent before it causes harm.

Remediation may include:

• Prompt refinement

• Retrieval-source cleanup

• Stronger guardrails

• Updated escalation rules

• Tool permission changes

• Better user instructions

• Model changes

• Workflow redesign

After remediation, the agent should be retested to confirm improvement.

Step 5: Monitor for Drift

AI assurance should not end at launch. Agents should be retested when:

• The model changes

• The prompt changes

• New tools are connected

• New data sources are added

• The workflow expands to new users

• Business policies change

• A failure or near miss occurs

This helps teams catch behavioral changes before they affect users.

Step 6: Produce Evidence

For PE firms, boards, auditors, and buyers, evidence matters. SureWire helps organizations move from informal confidence to documented assurance.

That evidence can support:

• Internal governance reviews

• Board reporting

• Compliance programs

• Customer assurance

• Vendor risk management

• M&A diligence

• AI risk management programs

From AI Adoption to AI Assurance

Many organizations are already using AI. The next maturity step is proving that those AI systems can be trusted.

That requires a shift from informal experimentation to structured assurance.

AI Experimentation	AI Assurance
“We tried it and it worked.”	“We tested it against known risks.”
Manual prompt checks	Repeatable AI agent testing
Limited internal demos	Realistic and adversarial scenarios
No consistent evidence	Documented quality and risk reports
One-time validation	Ongoing drift detection
Function-by-function adoption	Portfolio-wide governance model

SureWire helps organizations make this transition.

The Business Case for PE Firms

For private equity firms, AI assurance is not just a technical control. It is a value-protection mechanism:

Protect Revenue

AI agents used in sales, support, and customer success can directly affect customer experience and commercial outcomes. Testing helps reduce the risk of incorrect claims, poor customer interactions, and inconsistent messaging.

Reduce Operational Risk

AI workflows can create rework, escalations, exceptions, and process failures if they are deployed without validation. Testing helps identify these problems before they scale.

Strengthen Compliance Posture

Portfolio companies in regulated or data-sensitive industries need to show that AI is being used responsibly. SureWire helps create evidence that AI agents have been evaluated for safety, consistency, and compliance.

Improve Exit Readiness

As AI becomes embedded in operations, buyers and auditors may increasingly ask how AI systems are governed, tested, and monitored. Companies with documented AI assurance practices will be better prepared for diligence.

Scale AI with Confidence

The goal is not to slow down AI adoption. The goal is to make it safer, more repeatable, and more defensible.

Why Inflectra

Inflectra has a long history in software quality, lifecycle management, test management, automation, risk management, and enterprise delivery. SureWire extends that quality engineering discipline into the age of AI agents.

As organizations adopt AI across development, GTM, marketing, support, and administrative workflows, quality assurance must evolve. AI agents require testing approaches built for non-deterministic behavior, real-world scenarios, adversarial inputs, and ongoing drift.

SureWire brings Inflectra’s quality-first philosophy to one of the most important enterprise technology shifts of the decade.

Conclusion

AI Can Create Enterprise Value. SureWire Helps Protect It.

Private equity firms are right to see AI as a major lever for operational improvement and value creation. AI agents can help portfolio companies move faster, operate more efficiently, and scale expertise across the business.

But AI adoption without assurance introduces risk.

Agents can hallucinate. They can leak data. They can behave inconsistently. They can be manipulated. They can drift after changes. And they can affect customers, employees, revenue, compliance, and reputation.

SureWire™ helps organizations address these risks directly. By testing AI agents for safety, consistency, compliance, and reliability, SureWire gives PE firms and portfolio companies a practical path from AI experimentation to AI assurance.