Rise of the AI Developer: Vibe Coding and Agent Teams Rewrite Software and Security

How AI Is Changing the Software Development Process-and the Product

AI is changing how software gets built and what software is made of. For product teams, this means faster delivery, new interface patterns, and a different risk profile to manage.

Developers now work with AI as a partner. The role shifts from writing every line to directing systems, reviewing outputs, and integrating AI-native components into the product.

AI changes the software development process

The rise of the AI developer

Code assistants complete functions, write tests, and suggest fixes. Developers spend more time on architecture, data contracts, and reviews-and less time on repetitive coding.

Adoption is widespread. A CodeSignal survey reports 81% of developers use AI coding assistants; 49% use them daily and 39% weekly. A Legit survey found 96% of security and software development professionals say their companies use GenAI to build or deliver apps, and 88% of developers use AI coding assistants.

• Implication for Product: plan capacity assuming higher throughput, but enforce quality gates to keep defect escape rates in check.
• Shift acceptance criteria from "what to build" to "what to verify" so AI output is testable by default.

Vibe coding moves work from typing to directing

Vibe coding lets developers describe intent in natural language and have tools generate and run the work. Tools like Cursor can scaffold features, write tests, and iterate on feedback.

Gartner projects that by 2028, 40% of new enterprise production software will be created with vibe coding techniques and tools.

• Use cases: prototyping, test generation, migration tasks, internal tools, and "glue code."
• Guardrails: lock down environments, pin dependencies, and require tests to pass before AI-generated code can merge.

Agentic workflows change how teams ship

"Teams" of AI agents can plan, implement, test, and document features. Each agent handles a step, then hands off to the next with context.

Agent usage is surging; daily active usage has more than doubled year over year, based on recent announcements at Microsoft's Build 2025.

• Structure epics into agent-friendly tasks with clear inputs, outputs, and checks.
• Enforce automated checkers: linting, test coverage thresholds, security scans, and policy-as-code before a merge.
• Track acceptance rate of AI suggestions and rework needed to understand true productivity gains.

AI changes the product itself

AI is now part of the stack: models, LLMs, and agents call your APIs, retrieve data, and trigger workflows. Interfaces shift from forms to chat, command palettes, and background agents.

AI is also a user of your product. Expect both internal and external agents to call your endpoints, scrape docs, and automate actions.

• Design for "agent personas" alongside human personas. Document supported intents and rate limits.
• Add telemetry and audit trails for AI-driven activity. You need attribution for who/what did what, when, and why.
• Treat RAG and tool usage as product features: define sources of truth, freshness SLAs, and fallback behavior when confidence is low.
• Expose smaller, predictable APIs that are easy for agents to chain. Avoid brittle flows that depend on hidden state.

Security: new risks and new leverage

AI adds new attack surfaces-prompt injection, data leakage, model/API supply chain-but it also gives teams better detection, testing, and automation. Treat it as risk and advantage.

What Product can do right now

• Create an AI feature register: models used (and versions), prompts, tools/APIs called, data touched, and retention settings.
• Extend SBOMs with an "MBOM" for models and prompts. Pin versions, track evaluations, and document known limits.
• Ship an AI evaluation suite: prompt injection tests, jailbreak checks, toxicity filters, PII leakage checks, and grounding tests against your sources.
• Set policy for data: what can be sent to third-party models, redaction rules, and secrets handling. Make the default "private."
• Require human approval for high-impact actions (payments, data deletion, permission changes). Add just-in-time confirmations.
• Instrument everything: suggestion acceptance rate, agent task success rate, time-to-merge, escaped defects, MTTR for rollbacks.
• Compare AI-assisted vs. non-assisted work on scope accuracy and quality, not just speed.
• Run scenario-based risk reviews for AI features and schedule regular red-teaming of prompts and tools.
• Have fallbacks: degrade to deterministic flows when confidence is low or services are down.

Operating model and org

• Publish prompt and style guides with tested patterns for your codebase and product voice.
• Define an "AI maintainer" role to curate prompts, supervise agents, and own evaluations and incidents.
• Create safe sandboxes and data tiers so experimentation doesn't leak sensitive information.
• Upskill the team with focused training on LLM product patterns, secure use of code assistants, and agent orchestration. Explore practical paths at Complete AI Training by job and tool options for engineering at Generative Code tools.

Bottom line

AI shifts developers into director mode and puts AI-native components at the core of modern products. Your edge comes from clear problem framing, strict guardrails, and relentless measurement.

Treat AI as both constraint and multiplier. Build the controls, then press on delivery.