Are your AI agents production-ready?

As organizations adopt intelligent apps, a second wave of transformation is taking shape. AI now contributes not only to app features, but also to how apps are designed, built, and delivered. We are in the era of agentic delivery, where AI agents help analyze legacy systems, refactor code, and accelerate development.

Many organizations have begun exploring agent-driven workflows. The opportunity now is to bring these capabilities into production. Moving from experiments to delivery requires readiness in structure, governance, and team practices.

In our recent blog, People & Platform: Compound Value with Intelligent Apps, we looked at how engineering and experience evolve in parallel. Now, we elaborate more on how to operationalize agentic delivery—building the conditions for AI agents to work as active contributors within product teams.

Agentic delivery gains momentum when it becomes a team capability. Success depends on embedding it into delivery processes, supported by clear frameworks and ways of working.

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The new role of AI across the SDLC: think ‘team’.   

Agentic delivery is the next iteration of applying AI to code generation: autonomous systems to take on real, repeatable tasks across the development lifecycle. AI agents become contributors—they analyze systems, draft assets, suggest improvements—and are treated as part of the team’s workflow rather than as external helpers or services. And when a delivery pod adopts that model end-to-end, we call it an agentic squad — a cross-functional team that treats AI agents as accountable contributors, with defined prompts and review processes.

Here’s what that could look like in action:

• A developer uploads screenshots of a legacy application. An agent uses those to recreate front-end components based on recognized UI patterns.
• A team planning a migration project asks an agent to inspect source systems. It identifies dependencies, flags inconsistencies, and outputs a first-pass sequence for extraction and rebuild.
• During delivery, an agent is prompted with a design pattern. It proposes working code, complete with unit tests, ready for developer review.

In each of these cases, the agent is a shared delivery asset—something the team interacts with, relies on, and shapes through its prompts and usage patterns.

What sets this apart from earlier forms of automation is the degree of context awareness. Agents can respond to design intent, align with coding practices, and work with evolving inputs. That makes them suitable not only for experimentation, but for integration into active sprints – that is, if the organization is ready to support that model.

Agent-led delivery foundations in five pillars

For agentic AI to function in production (not just in pilots or labs), delivery organizations need to be structurally prepared – with an environment that enables agents to contribute safely, reliably, and repeatedly.

1. Team structure
   Agents won’t behave like teammates, yet they’ll affect timelines, quality, and scope. Teams need clarity on where and when agents are appropriate. They also need simple, workable ways to review the outputs and fold them into sprint rhythms. Delivery pods need clarity around who owns the outputs, how agent interactions are managed, and where responsibilities lie.
2. Governance
   Agent output must meet the same expectations as human work. That means planning for validation steps, data controls, and policy checks.
   
   Agents trained on enterprise data need clear access protocols. Prompts that touch sensitive content need boundaries. Outputs that affect delivery need a validation layer.
   
   Governance needs to be in place before agent outputs enter production. Enterprises that scale agentic delivery well typically establish usage patterns early: what agents can access, what they can’t, and how human review is built in.
3. Technical infrastructure
   An agent can only go as far as the platform lets it. Without consistent environments and clear boundaries, AI contributions are hard to manage. Cloud-capable infrastructure, automated provisioning, and well-defined permissions help avoid surprises, and support scale.
   
   Whether hosted in Microsoft Azure or in hybrid environments, the architecture should support responsive compute and production-grade observability.
4. Processes and practices
   Agents won’t behave like teammates, yet they’ll affect timelines, quality, and scope. Teams need clarity on where and when agents are appropriate. They also need simple, workable ways to review the outputs and fold them into sprint rhythms. Delivery pods need clarity around who owns the outputs, how agent interactions are managed, and where responsibilities lie.
   
   Reviews need a way to distinguish between human-built and agent-generated work, and decide what to accept, refine, or reject. Teams also need practical ways to document how agent outputs were used, so that future teams learn what prompts, conditions, and review steps helped produce good results.
5. Enablement
   Agents don’t replace developers or testers—but they do change how work gets done. Teams need enough exposure and support to feel confident. This doesn’t always require formal upskilling, rather – start with small, structured opportunities to experiment, review, and improve together.
   
   Enablement includes access to safe sandboxes, prompt libraries, and internal showcases of what’s possible. And adjusting incentives. If agentic productivity isn’t recognized in sprint goals, teams won’t invest in it. Distributed enablement—led by embedded coaches or power users—often works better than top-down mandates. Eventually, agentic squads work side-by-side with AI seamlessly.

Should innovation islands be steering the ship?

Many organizations begin their AI journey with a central team tasked with exploring use cases and building early prototypes. That model works well for initial experimentation—but it rarely scales. When all expertise sits in one place, delivery bottlenecks emerge. Teams in the field wait for direction, and valuable knowledge fails to reach those doing the actual work.

To get real value from agentic AI, teams across the organization need working exposure. That means enabling them to test, apply, and improve intelligent delivery methods in their own environments. Adoption improves when developers and testers can experiment within live projects, not just in demos or training sessions.

This thinking doesn’t require a complete reorganization, but it does start by embedding agentic ways of working inside existing delivery structures. Pods or squads that already own parts of the lifecycle can build in AI usage—prompting agents, validating their outputs, and shaping shared practices over time.

Jumpstart from ideation to delivery.

Agentic delivery often starts with discovery. Teams need to understand what agents can do, how they behave in real workflows, and where they can drive meaningful outcomes. Structured ideation formats—like Sogeti’s Jumpstarts—can help teams move quickly from theoretical use cases to hands-on validation. Short, high-impact engagements bring business owners, designers, and technical leads together to:

• Identify agent-ready use cases.
• Align them to business priorities.
• Build working prototypes using proven components.

But ideation alone isn’t enough: what sets successful organizations apart is how they carry that momentum forward—operationalizing what they’ve learned across delivery teams. That means:

• Pairing discovery sessions with readiness assessments.
• Establishing platform standards and security baselines early.
• Ensuring delivery pods are equipped to use agents safely and meaningfully from the start.

When ideation leads directly into team enablement, teams can skip the trial-and-error phase and move faster toward intelligent delivery at scale.

Make way: Agentic delivery is the next step in intelligent work.

AI is already influencing how applications are imagined, developed, and shipped. The next opportunity is to shape a delivery model that reflects that reality. That means creating space for agents to contribute meaningfully, within environments that are safe, governed, and connected to real business needs.

Agentic delivery is a shift in how delivery teams operate. When roles, platforms, and practices are aligned, intelligent work becomes part of the flow. What starts as a pilot or prototype becomes a standard, repeatable capability.

Lead this shift, and you won’t be simply using AI—you’ll be working alongside it.

Ready to move from pilots to production?

Sogeti helps enterprises turn AI agents into everyday delivery assets—embedded within teams, backed by cloud-native architecture, and governed for scale.