In today’s digital landscape, the ability to produce software with speed and predictability has become a critical factor in competitiveness. In recent years, many organizations have taken their first steps by incorporating AI into development—for example, using assistants like GitHub Copilot. However, we are entering a deeper paradigm shift: the transition from AI-assisted development to hybrid team models, where people and AI agents collaborate across the entire software development lifecycle (SDLC).
These agents no longer just suggest code. They can execute complex tasks, such as analyzing requirements, implementing changes, reviewing code, running tests, identifying security risks, or monitoring production behavior—always within defined rules and controls. This agentic development model redefines how work is organized and how productivity is measured.
The problem is that many organizations believe they’ve already “adopted AI” when, in reality, they’ve only implemented isolated tools without modifying their operating model. Remaining in this halfway point is risky: it may bring speed to certain areas but increases variability, team friction, and the difficulty of maintaining control and quality at scale.
At Baufest, we see this shift not merely as a technological upgrade but as a comprehensive redesign of the SDLC. The true challenge is to scale productivity without compromising governance, predictability, or quality—especially in complex or regulated environments.
There’s strong belief in the productivity impact of generative AI in software development—and data backs it. However, in practice, the biggest improvement potential lies not in the code itself, but in all the work surrounding it. Planning, review, testing, integration, security, and operations often account for up to 80% of a project’s total effort, according to research from IEEE, GitHub, and Microsoft.
For years, that 80% was handled with manual processes or rigid automations: scripts, pipelines, and static rules. The agentic approach introduces a qualitative leap. Instead of automating isolated steps, objectives are delegated to AI agents that can execute, verify results, react to deviations, and escalate issues when needed. This enables intelligent intervention in traditional bottlenecks.
According to McKinsey estimates, the impact of generative AI on software development productivity ranges between 20% and 40%. At Baufest, we’ve observed improvements within that range—when AI and agents are integrated end-to-end into the SDLC, maintaining or even improving quality levels. The key isn’t speeding up a single link—it’s redesigning the entire system.
Three pillars for an agentic SDLC: people, processes, and platforms
The speed unlocked by AI doesn’t just accelerate delivery—it also accelerates the emergence of problems. When processes, people, and platforms don’t evolve in sync, errors and risks arise faster and hit harder.
To ensure AI agent adoption doesn’t become a source of new issues, we address this transformation through three inseparable fronts:
1. People: in an agentic SDLC, developers and leaders stop performing repetitive tasks and shift to creative roles focused on design, prioritization, and control. This requires preparing teams to work with agents, defining which decisions can be delegated and which remain under human oversight—avoiding uncertainty or loss of trust due to AI autonomy. The goal is to improve the developer experience and help teams reach a “flow state,” focusing on high-value problems.
2. Processes: incorporating agents means redesigning the SDLC so they operate within clear guardrails for security, quality, and compliance. Rather than adding controls at the end, the SDLC is built from the ground up with explicit rules, policies, and validations that agents consistently follow. This is especially crucial in regulated industries like banking or insurance, where speed without control can quickly lead to serious incidents.
3. Platforms: agents need context to operate effectively: code, change history, organizational rules, workflows, and operational signals. Maximizing the value of platforms like GitHub means using them as a control plane, where tools like Copilot, Actions, and Advanced Security converge—avoiding fragmented implementations that prevent agentic models from scaling coherently.
These three pillars work as a system. If one fails, agent-based productivity becomes unstable or degrades entirely.
From AI usage to operating with AI Agents
Adopting an agentic SDLC isn’t about replacing people—it’s about reorganizing work. Some operational activities (review, testing, security analysis, CI, monitoring) can be partially or fully handled by agents. The human role shifts to defining objectives, validating outcomes, making complex decisions, and governing the system.
This change raises an inevitable question: how can you tell if agents are actually improving productivity and not just adding complexity?
Our answer starts with a simple premise: an agentic SDLC only scales if it’s measurable. That’s why we combine standard industry metrics with AI-specific adoption and behavior indicators, building an integrated control dashboard that includes:
- DORA Metrics: Deployment frequency, change lead time, failure rate, and mean time to recovery (MTTR).
- Delivery and Flow Metrics: Lead time and throughput, to verify whether teams are delivering faster without compromising standards.
- Developer Experience: Team satisfaction levels—since agents should reduce repetitive work, not increase cognitive load.
- AI Adoption and Usage Metrics: Utilization rate, costs, and agent coverage—to ensure the model is being adopted consistently and sustainably.
Without this approach, an agentic implementation can increase volatility, leading to more incidents and less predictability.
That’s why the key is not offering an abstract promise, but achieving a concrete operational shift: moving from simply using AI to operating with AI agents in a secure, scalable, and measurable way. The future of digital product development no longer lies in individuals working alone or in isolated tools.
It lies in organizations capable of aligning talent, processes, and AI agents within a coherent model. Those that succeed will not only develop faster — they will build a sustainable competitive advantage in 2026 and beyond.
