Foundation trends: The tectonic shift
5 min readTrend 1: The software development lifecycle changes dramatically
The way we interact with computers is undergoing one of its most significant changes since the graphical user interface. From machine code to assembly to C to modern high-level languages, each abstraction layer reduced the gap between human thought and machine execution.
The most recent step in this evolution was human/machine conversation. In 2025, agentic AI changed how a large swath of developers write code. 2026 is poised to be the year when the systemic effects of this evolutionary shift reconfigure the software development lifecycle and reshape software engineering roles.
| Traditional SDLC (Weeks-Months per cycle) | Agentic SDLC (Hours-Days per cycle) |
|---|---|
| 1. Requirements and planning (Days-Weeks) | 1. Express intent (Minutes) |
| 2. System design (Weeks) | 2. Agent understands (Seconds) |
| 3. Implementation and coding (Weeks-Months) | 3. Agent implements (Minutes) |
| 4. Testing and QA (Days-Weeks) | 4. Agent tests + docs (Minutes) |
| 5. Code review (Days) | 5. Human review (Min-Hours) |
| 6. Deploy and release (Days) | 6. Deploy and ship (Minutes) |
| 7. Monitoring and observability (Ongoing) | 7. Monitoring and observability (Continuous) |
| 8. Feedback and iteration (Continuous) | 8. Learn and iterate (Ongoing) |
| Traditional SDLC | Agentic SDLC |
|---|---|
| Sequential handoffs | Fluid agent flow |
| Human codes everything | Human guides, agent executes |
| Docs as afterthought | Docs generated inline |
| Manual incident response | Agent-assisted remediation |
Traditional SDLC stages remain, but agent-driven implementation, automated testing, and inline documentation collapse cycle time from weeks to hours. Monitoring feeds directly back into rapid iteration.
Predictions
- Evolution of abstraction: Most of the tactical work of writing, debugging, and maintaining code shifts to AI while engineers focus on higher-level work like architecture, system design, and strategic decisions about what to build.
- Engineering role transformation: Building software used to mean primarily writing code, although software engineering roles always involved many other skills. Now, being a software engineer increasingly means orchestrating agents that write code, evaluating their output, providing strategic direction, and ensuring the system as a whole solves the right problems correctly.
- Expedited onboarding to dynamic project staffing: Traditional timelines for onboarding to a new codebase or project will collapse from weeks to hours, changing how companies think about talent deployment and project resourcing.
The collaborative reality
While agents handle more implementation work, the nature of this shift reveals something important: engineers are becoming more "full-stack" in their capabilities rather than being replaced. Our research (opens in new tab) shows engineers can now work effectively across frontend, backend, databases, and infrastructure—areas where they may have previously lacked expertise—because AI fills in knowledge gaps while humans provide oversight and direction.
This capability expansion enables tighter feedback loops and faster learning. Tasks that once required weeks of cross-team coordination can become focused working sessions. Engineers describe using AI for tasks that are easily verifiable, well-defined, or repetitive, while keeping high-level design decisions and anything requiring organizational context or "taste" for themselves.
Role transformation: From implementer to orchestrator
In 2026, the value of an engineer's contributions shifts to system architecture design, agent coordination, quality evaluation, and strategic problem decomposition. The primary human role in building software is orchestrating AI agents that write code, evaluating their output, providing strategic direction, and ensuring the system solves the right problems for the right stakeholders. Engineers who master orchestration can shepherd multiple features through development simultaneously, applying their judgment across a broader scope than individual implementation previously allowed.
Onboarding revolution
In 2025, the traditional timeline for onboarding to a new codebase or project began to collapse from weeks to hours. In 2026, we anticipate organizations learning how to use this capability to its fullest, changing how companies think about talent deployment and project resourcing.
One manifestation we envision is dynamic "surge" staffing. Businesses will be able to surge engineers on-demand onto tasks requiring deep codebase knowledge. Organizations can start staffing projects dynamically, bringing in specialists for specific challenges and shifting resources without the traditional productivity dip.