We use Lean Product Management techniques to identify and prioritise AI agent opportunities aligned to your business strategy. For each priority use case, we define the agent's scope, tool set, inputs, outputs, and success criteria — turning a business opportunity into a designable system. Identified use cases are categorised and prioritised according to their complexity, value, and infrastructure readiness.
AI agents need to call your systems reliably, interpret responses accurately, and react to state changes in real time. We assess whether your platforms expose clean, versioned, well-documented APIs suitable for machine consumption; whether your architecture is event-driven where agents need to respond to change; and whether you have — or could implement — MCP servers to give agents structured, safe, auditable access to your systems and data.
Agents don't just need accurate data — they need accessible, real-time, agent-consumable data. We assess whether the data your priority agents need is available, structured, and trustworthy enough for autonomous action rather than just analysis. We evaluate your pipelines, data quality processes, latency characteristics, and the degree to which your data architecture can serve agents reliably at the speed and granularity they require.
Strong data governance is essential for AI agent adoption – but in an agentic context it takes on new dimensions. We assess your governance frameworks with the agent question specifically in mind: what can an agent see, what actions can it take, who authorises those actions, and how are agent decisions logged and audited? We identify gaps that could expose you to regulatory or reputational risk when agents act autonomously on your behalf.
Building AI agents is an engineering discipline, not just a tooling decision. We assess whether your teams have the capability to design, build, evaluate, and iterate on agents reliably – including prompt engineering, tool design, RAG implementation, agent orchestration, and the evaluation frameworks that tell you whether an agent is actually working safely before you put it in front of users or give it access to live systems.
We assess your organisation's readiness to execute AI agent initiatives — evaluating the knowledge, skills, and resources available across both engineering and business teams. We analyse your teams' experience with agentic AI specifically, review existing roadmaps and capacity constraints, and identify where additional expertise, training, or external partnership will be required to execute the identified use cases at the pace the business needs.
Understand how ready your software delivery lifecycle and codebase are for agentic AI, and get a prioritised roadmap showing exactly what needs to change to adopt it safely and at scale.
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Clear, in-depth assessment of your bespoke strategic software covering code quality, complexity, security risks, and a practical remediation plan.
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Get clear, in-depth and independent expert analysis of bespoke software products, people and processes to inform your investment decisions during M&A
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Data Collection: Through a series of metrics analyses and interviews, we gather extensive data on your technology landscape.
Take a look at how we’ve helped some of our clients tackle similar challenges.
Based on our experience in highly regulated environments, the Swiss multinational healthcare company Roche wanted to work with our craftspeople to build the next generation of their digital platforms and to improve their ability to continuously deliver quality systems.
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dsm-firmenich relied on Codurance’s expertise to scale the world’s first AI-based scent curation platform: Scentmate by dsm-firmenich. Optimising its search engine, training its team and achieving 99.95% stability of its system allowing them to boost its sales and customer loyalty.
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We accelerated the launch of UK based digital healthcare company, MyPulse, in different markets and future-proofed their digital platform through the use of XP best practices, cloud technologies and our functional languages experience.
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