AI in the Software Development Process: Acceleration Without Quality Trade-offs
Mar 4, 2026
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Обновлено
3.4.2026
In this article, we describe our 4-level model for embedding AI into the software development process. The model positions AI as a controlled enhancer within a quality-first SDLC, not an autonomous driver. Human oversight at critical points maintains system coherence and long-term viability.
The 2025 Stack Overflow Developer Survey reports that 84% of developers are using or planning to use AI tools in their development process, up from 76% the previous year. Yet trust has declined sharply: only 33% trust the accuracy of AI output, while 46% actively distrust it (up from 31% in 2024). Among developers with 10+ years of experience, high distrust reaches ~20%, with high trust at just 2.6%. Positive sentiment toward AI tools has fallen to 60%, down from over 70% in 2023-2024. And 75% of developers still consult a human colleague when they question AI’s answers.
This gap highlights a persistent challenge in enterprise settings. Teams adopt AI for speed, yet the output frequently requires extensive rework–producing fragile prototypes, architectural inconsistencies, and mounting technical debt. In high-stakes domains such as video platforms supporting millions of concurrent streams or AI inference systems handling petabyte-scale data, these issues translate directly to production incidents, security exposures, and elevated long-term costs. We have observed multiple “AI-first” initiatives generate code rapidly only to require months of stabilization before reliable deployment.
We use AI extensively in our daily work, but our integration differs from common approaches. AI serves as the most powerful accelerator available to engineering teams today. Without rigorous architecture, disciplined processes, and strict quality controls, however, it accelerates technical debt more than it accelerates delivery.
We never assign architectural ownership or system-level decisions to AI. We treat it as a capable assistant that extends our analysis and iteration speed, while humans retain full responsibility for final judgments – especially on structure, scalability, and maintainability.
In our agentic development process, AI supports the overall software development lifecycle rather than driving code generation from the start. We begin with detailed decomposition and specification; generation follows only after clear human-defined boundaries. This structured approach delivers measurable acceleration while preserving quality, particularly in complex, real-time, high-load systems such as WebRTC-based video platforms and AI inference pipelines.
Level 1: Discovery & Decomposition (Human + AI Collaboration)
We begin every project with rigorous discovery. Humans lead by defining business requirements, user needs, and constraints. AI assists in research, ideation, and breaking down complex problems into atomic components.
Mechanics: We feed AI curated prompts with initial specs, e.g., "Decompose a WebRTC-based video conferencing system into core modules: signaling, media transport, scalability layers." AI generates initial breakdowns, highlights edge cases (like network jitter in high-load scenarios), and suggests research from reliable sources. We then refine manually, ensuring alignment with enterprise standards.
This level avoids "context rot"–the degradation in AI performance with overly long inputs, as noted in studies like "Lost in the Middle" by Liu et al. (Stanford & Berkeley). By bounding contexts to specific sub-problems, we maintain focus.
Real-world example: In a current project building a personalized AI stylist application (with strong inference components for mobile/edge deployment), users upload wardrobe photos, and the system must recognize fine-grained attributes: garment type, season suitability, color palette, fabric texture, sleeve length, and neckline style. The AI then generates occasion-aware outfit recommendations factoring in weather data, user preferences, and calendar events.
We started with human-led decomposition of the recognition pipeline. Qwen assisted by suggesting optimized architectures, combining TensorFlow Lite + YOLOv8m for detection, Vision APIs for attribute extraction, PyTorch for fine-tuning, and CLIP embeddings for semantic matching. It helped curate and parameterize a custom dataset focused on traditional and culturally specific clothing (poorly covered by off-the-shelf models), reducing dataset preparation and experimentation time from weeks to days. Human engineers retained full ownership of the training spec, validation strategy, and edge-case handling, ensuring production reliability under variable lighting and device constraints.
We consciously limit AI here to augmentation. It doesn't own requirements; that's human territory to ensure strategic fit.
Level 2: Architectural Layer (Human-First with AI Validation)
Architecture is sacred – 100% human-owned. We design the system's blueprint first, then use AI strictly for validation and stress-testing.
Mechanics: Engineers craft diagrams and specs (e.g., using UML or simple flowcharts). AI then simulates loads, analyzes scalability, and probes for weaknesses. For instance, we prompt: "Validate this microservices architecture for a video streaming platform: assume 1M concurrent users, identify single points of failure."
This draws on tools like AI scalability analysis to model scenarios, but we never let AI propose the core design. As Anthropic's 2025 guidance emphasizes, LLMs have finite attention budgets; we keep inputs targeted to avoid dilution.
Our 4-level Agentic Engineering Model
Real-world example: For existing projects with large codebases and well-established architectures, AI plays a complementary role: it cross-checks adherence to our internal documentation, surfaces any drift from defined patterns, identifies newer best practices (e.g., updated codec resilience for AV1 in high-loss environments or improved ICE candidate prioritization), and highlights incremental improvements without proposing wholesale redesigns. Human architects always make the final call on whether and how to apply suggestions, preserving long-term system coherence and avoiding unintended complexity.
Contrast this with AI-first pitfalls: We've audited systems where models "designed" architectures, leading to over-complexity. In one anti-case, a telemedicine platform using LiveKit for one-on-one video calls ballooned to 4,000 lines of code – far beyond the typical 300 lines needed. It was riddled with random hardcoded values and contradictory commands, like "if something happens, end the call" alongside "if something happens, reconnect." This conflicting logic caused frequent crashes, amplifying technical debt in a high-stakes environment.
We do not use AI for initial architecture. Delegating here risks incoherent systems, as evidenced by METR's 2025 studies showing AI's sharp drop in success on tasks exceeding 4-hour human complexity.
Level 3: Controlled Code Generation (AI as Executor Under Human Guardrails)
With architecture locked, we move to implementation. AI generates code in bounded, reviewed segments – our "agentic code generation" approach.
Mechanics: We provide precise specs per module, e.g., "Generate TypeScript for WebRTC peer connection setup, adhering to this architecture diagram and security constraints." Output is versioned, diffed against standards, and human-reviewed before merge. This controls quality, reducing issues like the 1.7x higher defects in AI-generated PRs reported by CodeRabbit's 2025 analysis of 470 open-source pulls.
Real-world example: In a Node.js + Express + MongoDB backend paired with a mobile app, we used AI agents to handle targeted debugging after initial deployment. Vague tickets like “The button does not work” produced useless suggestions. With full context: error logs, stack trace, and code (“Save” action returning 500 on valid input), the AI traced the issue to an unhandled async error in middleware and proposed a fix aligned with our error-handling invariants.
We cap AI's scope to atomic tasks, per Scale AI's SWE-Bench Pro 2025 leaderboard (top models at ~23% success on multi-file enterprise tasks). This prevents churn, as GitClear's 2025 research notes a rise in duplicate code and declining refactoring in AI-heavy workflows.
Level 4: AI as QA & Analysis Engine (Post-Implementation Audit)
Finally, AI serves as a relentless auditor for testing, reviews, and debt detection.
Mechanics: We deploy AI for automated code reviews, test case generation, and regression suites. Prompts like "Analyze this AI inference codebase for performance inefficiencies under load" flag issues early. This integrates with CI/CD, catching logic errors (75% more common in AI-gen code, per CodeRabbit) before production.
Real-world example 1: In a project involving real-time social survey processing (high-throughput backend with Elastic ELK Stack and Datadog monitoring), we used AI agents to triage production incidents post-deployment. For stack traces reaching 10,000+ lines per second bursts, the AI formed 3-5 probable root-cause hypotheses and auto-structured error reports with relevant logs and code context.
Human engineers reviewed and prioritized. Average triage time dropped from 45 to 18 minutes per defect (-60%), and "insufficient information" returns from developers fell by 33%. This kept incident resolution predictable in a system handling continuous high-load data ingestion.
Real-world example 2: Before implementation, we always start with human-led decomposition of product risks and testing strategy. AI then assists: suggesting structured scenarios, boundary/negative cases, coverage matrices, and spotting ambiguous requirements.
For 25 user stories in an authorization module (using models from OpenAI and Anthropic), AI generated 312 test cases in ~2 hours (vs. usual 2-3 days). Boundary coverage rose from 68% to 91% per our traceability matrix; clarifying questions to analysts increased 40%, uncovering 7 logical contradictions pre-development. Outputs were formatted for direct Jira/TestRail import, cutting documentation effort by ~60%. Human validation ensured alignment with system invariants – no delegation of risk decisions.
Real-world example 3: During exploratory sessions for a mobile video-calling app (WebRTC-based, high-load network variability), AI proposed heuristics for "breaking" the system and non-standard scenarios. It surfaced 42 additional stress cases (e.g., low bandwidth + orientation changes + session drops) and 11 defects missed by standard checklists.
When reviewing Figma layouts, AI flagged 6 missing states (empty/error/loading) and 4 UX issues pre-coding. This reduced UI defects at UAT by 25%. All findings went through human prioritization and confirmation to maintain architectural coherence.
We avoid over-reliance: AI flags, humans fix. This aligns with our enterprise AI development workflow, prioritizing maintainability.
Addy Osmani of the Google Chrome team nails it in his 2025 writings: AI gets you to ~70% of the task quickly, but the last 30%: edge cases, integrations, scalability, drives most long-term costs. We don’t use AI to write 70% of the code faster. We use it to radically reduce risk in the dangerous last 30%, where 80% of future pain lives.
AI-first accelerates the beginning of the task. Agentic accelerates (and protects) the lifetime of the system. Speed ≠ effectiveness. Speed × rework = hidden cost.
This contrast is stark:
The 70/30% Comparison Table
(Source: Adapted from our internal benchmarks, aligned with GitClear and CodeRabbit 2025 reports.)
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this is defenetely what i was looking for. thanks!
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Can you please provide example for flutter as well . I'm having issue to screen share in IOS flutter.
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Thank you Joy! Glad to be helpful :)
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I stumbled upon this guide from Fora Soft while looking for insights into making estimates for software development projects, and it didn't disappoint. The step-by-step breakdown and the inclusion of best practices make it a valuable resource. I'm already seeing positive changes in our estimation accuracy. Thanks for sharing your expertise!
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Please, could you fix the Kit Download link?. Many Thanks in advance.
Fora Soft Team
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We fixed the link, now the library is available for download! Thanks for your comment
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