AI agents are moving from experimental tools to business-critical assets. For investors, especially in M&A contexts, this technology represents both immense opportunity and potential hidden risk.
A robust Tech Due Diligence (Tech DD) now needs to go beyond the traditional review of code, infrastructure, and team capability—it must examine the entire lifecycle of AI agent development and operation. The 6 Stages of AI Agent Development (Planning, Design, Development, Testing, Deployment, Maintenance) offer a practical framework for assessment.
1. Planning – The Business Case & Strategic Fit
Investor lens:
- Does the AI agent solve a real business problem or is it a tech vanity project?
- Are the agent objectives aligned to the acquirer’s strategic goals?
- Has there been a risk & ethics review to mitigate compliance and reputational issues?
Risks: Poorly defined objectives or overpromising capabilities.
Opportunities: Early clarity ensures a direct link between AI agent performance and value creation.
Strengths indicator: Clear documentation of business needs, measurable KPIs, and resource planning.
2. Design – Guardrails & Architecture
Investor lens:
- Are there design guardrails to ensure safe, compliant outputs?
- Is there context grounding so the AI understands the operational environment?
- Has the team selected the right model and framework for scalability and interoperability?
Risks: Weak design guardrails can lead to unsafe or biased outputs.
Opportunities: Thoughtful architecture enables rapid adaptation to new markets and use cases.
Threats: Dependency on proprietary frameworks without exit strategies.
Strengths indicator: Modular, framework-agnostic design that can evolve with market needs.
3. Development – Building the Core
Investor lens:
- Is the agent logic clearly documented and maintainable?
- Have models been integrated and fine-tuned effectively?
- Is there a consistent process for documentation and knowledge transfer?
Risks: Black-box code that’s reliant on key individuals.
Opportunities: Strong documentation reduces operational risk during transitions.
Strengths indicator: Transparent build process, use of industry best practices, and model adaptability.
4. Testing – Proving Reliability
Investor lens:
- Are edge cases tested to failure?
- Have user experience tests confirmed adoption potential?
- Has the system passed integration and performance tests?
Risks: Skipping edge case testing can cause catastrophic production failures.
Opportunities: Comprehensive testing creates investor confidence in scale-up potential.
Threats: Regulatory fines if testing doesn’t meet compliance standards.
Strengths indicator: Clear test reports, traceable defect resolution, and compliance validation logs.
5. Deployment – Operational Readiness
Investor lens:
- Are guardrails actively monitored post-launch?
- Is there robust observability for real-time tracking?
- Has compliance validation been achieved in relevant jurisdictions?
Risks: Launching without monitoring exposes the business to reputational and financial damage.
Opportunities: Strong post-deployment processes enable quicker market entry and scaling.
Strengths indicator: Well-documented launch plans with rollback options.
6. Maintenance – Sustaining Value
Investor lens:
- Is user feedback actively shaping updates?
- Are operations optimised to control cost?
- Is there ongoing objective monitoring to ensure the AI agent is still relevant?
Risks: Complacency leading to model drift and irrelevance.
Opportunities: Continuous improvement cycles keep the technology ahead of competitors.
Strengths indicator: Measurable impact metrics tied to ROI.
Why This Matters in Tech DD
When an investor acquires a business with AI agents in play, they’re not just buying software—they’re buying an evolving ecosystem.
Assessing AI agents across all six stages reveals:
- Operational readiness: Can the AI agent deliver value day one?
- Strategic alignment: Will it accelerate growth in the investor’s portfolio?
- Risk mitigation: Are the guardrails strong enough to prevent reputational harm?
Investor Takeaway
The AI agent lifecycle isn’t just a developer’s blueprint—it’s an investment due diligence checklist. In M&A, the winners will be those who spot the AI assets that are built to last, ready to scale, and designed to adapt.
The opportunity is huge, but so is the cost of getting it wrong. In AI, due diligence isn’t about asking “What does it do?”—it’s about asking “How is it built, tested, and maintained to keep delivering tomorrow?”
