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Software engineering is likely to involve more orchestration as AI takes on or assists with implementation: defining outcomes, breaking work into bounded tasks, coordinating tools or agents, and checking what they produce. That points to a change in the mix of engineering work—not proof that developers will stop coding or that every company will change at the same pace.
What “software orchestration” means
Orchestration is directing AI-enabled tools and workflows toward an engineering goal. In practice, that can mean supplying context and constraints, assigning bounded work, coordinating steps, verifying outputs, and deciding when a person needs to intervene. It is broader than prompting for a code snippet, but it does not necessarily mean running a team of autonomous agents.
The OECD describes a spectrum: reactive agents, task-focused copilot workflows, and systems that coordinate multiple agents and workflows with less oversight. A coding assistant that helps with one task sits at a different point on that spectrum from a system that plans and delegates across a larger workflow.
Will AI replace software developers?
The evidence here supports a possible shift in responsibilities, not a conclusion that software developers will disappear. Gartner forecasts that AI-native engineering will embed AI across the software development lifecycle and shift developers’ work from implementation toward orchestration, problem solving, system design, and quality outcomes. That is a forecast, not a settled description of every engineering team.
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Gartner also forecast that 90% of enterprise software engineers would use AI code assistants by 2028, compared with less than 14% in early 2024. It forecast that at least 55% of software engineering teams would actively build LLM-based features by 2027, and that 70% of organizations with platform teams would include GenAI capabilities in internal developer platforms by 2027. These are Gartner predictions, not observed outcomes for those future dates.
Observed workplace evidence is more limited. In a study fielded in August 2025, Anthropic surveyed 132 engineers and researchers, conducted 53 in-depth interviews, and examined internal Claude Code usage. Between February and August 2025, the share of transcripts associated with new-feature implementation rose from 14.3% to 36.9%, while the share associated with code design and planning rose from 1.0% to 9.9%. These percentages describe the relative distribution of usage-task transcripts, not the share of engineers’ total time or a measure of output. Anthropic said the change could reflect model capability, adoption patterns, or both, and cautioned that its early-adopter findings may not generalize to other organizations.
What developers may do when AI writes more code
As implementation assistance grows, engineers may spend more attention on work that frames and governs the implementation. Gartner identifies orchestration, problem solving, system design, and quality outcomes as likely areas of emphasis. A practical reading of that forecast is that developers will still need to understand what the software should do, how it fits into a system, and whether the result is safe and correct.
- Define the outcome: Turn a broad request into requirements and acceptance criteria that can be checked.
- Decompose the work: Separate a large change into bounded tasks with clear inputs, dependencies, and expected outputs.
- Provide context and constraints: Make relevant architecture, conventions, data boundaries, and operational requirements explicit.
- Verify the result: Review code and behavior, run appropriate tests, and assess whether the solution meets the intended need.
- Retain engineering judgment: Make system-level trade-offs and take responsibility for decisions where goals are ambiguous or consequences matter.
This is not a claim that every developer’s job description will change in the same way. It describes the kinds of work that become more important when generating code becomes easier than determining whether the code is the right solution.
Why AI can amplify problems as well as productivity
DORA’s 2025 report describes AI’s primary role in software development as “that of an amplifier.” Its research included more than 100 hours of qualitative data and nearly 5,000 survey responses from technology professionals. The practical implication is that generating code faster does not, by itself, fix unclear ownership, weak coordination, or unreliable delivery processes. AI can reinforce the strengths of a well-functioning organization and magnify dysfunction in a struggling one.
Tooling and governance are part of the same challenge. GitLab’s release of its 2025 Harris Poll survey reported responses from 3,266 DevSecOps professionals and raised concerns involving fragmented toolchains, compliance, human review, and platform approaches. These are findings from a company-sponsored survey, not population-wide estimates. GitLab’s chief product and marketing officer, Manav Khurana, said: “Toolchain fragmentation has created bottlenecks for developers, and AI agents are amplifying the issue.”
How teams can decide what to delegate
Choose an autonomy level based on how consequential the task is and how readily its output can be checked. Gartner recommends balancing automation with human oversight according to business criticality, risk, and workflow complexity. The OECD identifies orchestration, monitoring, security, and development as relevant agent-tool categories, and highlights privacy, security, and accuracy as concerns. Its adoption evidence is indicative rather than exhaustive.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →| Decision factor | Question for the team | What it means for delegation |
|---|---|---|
| Autonomy and criticality | How much independent action is acceptable, and what is the impact of a mistake? | Keep consequential decisions under closer human control. |
| Verifiability | Can tests, review, or another independent check establish whether the result is correct? | Delegate more readily when outputs can be checked reliably. |
| Workflow complexity | Does the work have clear steps and dependencies, or require broad judgment across a system? | Use tighter boundaries and more checkpoints as complexity rises. |
| Context needs | Does the task depend on architecture, business rules, or organizational knowledge? | Supply that context explicitly and keep people involved where it is difficult to encode. |
| Data and compliance exposure | Could the workflow expose sensitive data or create security or compliance risks? | Set access limits and review requirements appropriate to the exposure. |
| Approval and escalation | Who must approve the output, and when should the workflow stop for human intervention? | Define approval and escalation points before increasing autonomy. |
A useful operating principle is to delegate bounded work that can be readily checked, while keeping ambiguous goals, system-wide trade-offs, organizational context, consequential risks, and final accountability with people. That is a practical decision rule, not evidence that every organization has adopted the same division of labor.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What adoption numbers do—and don’t—tell us
Different sources measure different populations, time periods, and behaviors, so their percentages should not be combined into a single adoption curve. Gartner’s figures are forecasts. Anthropic’s task shares come from usage at one company. GitLab reports a survey of DevSecOps professionals. The OECD’s 2026 discussion draws partly on Stack Overflow’s 2025 survey, which received more than 49,000 responses from 177 countries; for the agent-use finding, the relevant question had 31,890 respondents. About half of those respondents said they were using or planning to use agents, while 38% reported no plans. The overall survey total is not the denominator for that finding.
The OECD presents the adoption evidence as indicative and incomplete. Anthropic likewise cautioned that its findings may not generalize beyond its early adopters, saying: “It’s still early days—Anthropic has many early adopters internally, the landscape is rapidly changing, and our findings likely don’t generalize to other organizations or contexts right now.” Together, these qualifications matter: reported use and forecasts describe different things, and neither establishes a universal timetable for changing engineering work.
How to prepare without assuming coding is over
For individual developers, the durable response is to strengthen the skills that help turn generated work into dependable software: defining requirements, understanding system design, evaluating outputs, testing behavior, and recognizing risks. For engineering leaders, the corresponding task is to improve the workflow around the tools—clear ownership, well-defined tasks, appropriate review, and secure access to context. DORA’s findings make a key distinction: more capable tools do not substitute for effective organizational practices.
The direction of travel may be toward more AI-assisted implementation and more human attention to orchestration and quality. How far that goes will depend on the organization, the work, and the safeguards required; the available forecasts and early evidence do not establish that coding will disappear.
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