In 2025, coding agents moved from experimental tools to production systems that ship real features to real customers. Engineering teams discovered that AI can handle entire implementation workflows: writing tests, debugging failures, generating documentation, and navigating increasingly complex codebases.

In 2026, we expect single agents to become coordinated teams. Tasks that took hours or days may now complete with minimal human intervention — and engineers who once wrote every line of code will increasingly orchestrate long-running systems of agents so they can focus on architecture and strategy.

Yet a critical nuance has emerged: this transformation is fundamentally collaborative. Anthropic’s Societal Impacts research finds that while developers use AI in roughly 60% of their work, they report being able to “fully delegate” only 0–20% of tasks. AI is a constant collaborator — using it well still requires thoughtful set-up, active supervision, validation, and human judgment, especially for high-stakes work.

Three categories of trends

• Foundation trends — the tectonic shift we believe will reshape how development work happens.
• Capability trends — what we expect agents to be able to accomplish.
• Impact trends — what we anticipate will affect business outcomes and organizational structures.

The gap between early adopters and late movers is widening. Software development is evolving toward a model where human expertise focuses on defining the problems worth solving, while AI handles the tactical work of implementation. Let’s dive in.

These eight trends converge on a central theme: software development is shifting from an activity centered on writing code to one grounded in orchestrating agents that write code — while maintaining the human judgment, oversight, and collaboration that ensure quality outcomes.

The research is clear: AI is a constant collaborator, but using it effectively requires active supervision and validation, especially in high-stakes work. It’s not “fully delegated” but highly collaborative — and that distinction matters for how organizations approach adoption.

Four areas demand immediate attention

• Mastering multi-agent coordination to handle complexity that single-agent systems cannot address.
• Scaling human-agent oversight through AI-automated review systems that focus human attention where it matters most.
• Extending agentic coding beyond engineering to empower domain experts across departments.
• Embedding security architecture as part of agentic system design from the earliest stages.

Organizations that treat agentic coding as a strategic priority in 2026 will define what becomes possible; those that treat it as an incremental productivity tool will discover they are competing in a game with new rules. The key to success lies in understanding that the goal isn’t to remove humans from the loop — it’s to make human expertise count where it matters most.