Increase Claude Code Quality by 20%

We are officially deep into the Agentic Era of software engineering. As we highlighted in our recently published 2026 AI Tools for Software Development Report, AI coding assistants have evolved from simple autocomplete plugins into autonomous agents capable of planning, editing, and verifying multi-file tasks. Tools like Claude Code, Cursor, Windsurf, and Codex are fundamentally changing the way we build software.

But if you have spent enough time working with these agents, you have likely run into the same invisible wall that we did.

Our 2026 report uncovered a critical paradox: while AI coding tools excel on public datasets (scoring highly on SWE-Bench), their performance drops drastically to 20-30% on private, uncontaminated commercial codebases. Why? Because despite their brilliance, AI coding agents suffer from inherent limitations and biases. They hallucinate APIs, silently drift away from core requirements, and confidently write code that breaks existing architectures. It is a frustrating cycle that pulls engineers away from high-level architecture and drags them back into the weeds of debugging AI-generated implementation details.

At Shelpuk AI Technology Consulting, we build custom AI products under a fixed-price model. For us, development efficiency isn't just a nice-to-have; it's the foundation of our profitability. To solve these inherent AI flaws, we built an open-source Agentic Suite—a set of Model Context Protocol (MCP) servers and skills designed to eliminate model biases and enforce rigorous engineering discipline.

The results have been transformative. By deploying this tool-agnostic suite—which works seamlessly with Claude Code, Cursor, Codex, Gemini CLI, Antigravity, OpenClaw, and more—we've seen dramatic improvements in throughput and accuracy. One of our customers, Relevant Software, recently reported a 15–20% increase in AI code generation quality using this exact setup.

Here is a deep dive into the underlying problems holding AI agents back, and how our open-source suite fixes them.

Problem 1: Dataset Limitations and the "Knowledge Cutoff"

Large Language Models (LLMs) are pretrained on vast amounts of internet data. Consequently, they are exceptional at writing code for highly popular languages and widely used frameworks.

However, if you are working with less popular programming languages, proprietary internal APIs, or cutting-edge technologies that were released after the model's training cutoff, the LLM will severely underperform.

This could be fixed by forcing the agent to rely on the web search instead of the internal intelligence. However, internal web search as well as third-party MCP servers like Tavily suffer from several critical limitations.

Picture this: Your AI assistant is debugging a cryptic Google Cloud Batch error. It searches the web and finds the perfect StackOverflow thread. But standard web search MCP servers only return the URL and a brief snippet. The AI then has to make a second call to scrape the page, which often returns a mess of HTML navigation panels, ads, and noise that wastes context tokens. Crucially, it often misses the actual solution entirely.

The Solution: Kindly Web Search MCP Server

To address this, we developed the Kindly Web Search MCP Server.

Instead of generic scraping, Kindly integrates directly with the APIs of developer-centric platforms like StackExchange, GitHub Issues, arXiv, and Wikipedia. When your agent searches for an error, Kindly returns the full conversation in a single call—the original question, the code snippets, the accepted answers, the "this fixed it for me" comments, and the reactions—all formatted natively for an LLM.

By forcing your agent to validate solutions and API contracts through Kindly, you effectively bypass the LLM's knowledge cutoff and bridge the gap for niche or cutting-edge technologies, saving massive amounts of tokens and preventing hallucinations.

Problem 2: The "Bad Token" Problem (Self-Gaslighting)

LLMs generate text autoregressively—meaning they generate code token by token. By design, every new token is selected to make the sequence of previous tokens look plausible.

This creates a dangerous phenomenon we call The Bad Token Problem.

Think of it like starting a sentence with a wrong assumption; your brain automatically tries to make the rest of the sentence sound convincing, even if the premise is flawed. If an AI agent makes a questionable design choice early in a file (a "bad token"), every subsequent line of code it writes will attempt to justify and reinforce that initial mistake to maintain coherence. The agent effectively gaslights itself, dragging the entire implementation down the wrong path.

The Solution: Lad MCP Server (Dual-Reviewer AI)

How do human engineering teams solve tunnel vision? Peer review. We applied the same logic to AI.

We built the Lad MCP Server to allow your primary coding agent to request system design and code reviews from two other independent LLMs via OpenRouter (e.g., moonshotai/kimi-k2.5 and z-ai/glm-5). By running two state-of-the-art reviewers in parallel, Lad reduces individual model bias. Because these reviewer LLMs are evaluating the finished diff—and are not burdened by the token-generation history of the original author—they easily spot the coherence-driven mistakes the first agent was blind to.

The Missing Link: Project-Awareness via Serena

A major flaw with standard AI code review is that the AI reviews the diff in a vacuum. A human engineer checks a PR against requirements, team constraints, and prior architectural decisions. An AI usually just checks if the syntax runs.

To fix this, Lad connects directly to Serena, an open-source semantic code navigation MCP that acts as a "headless IDE" for AI. Serena provides the Lad reviewers with persistent project memory and a complete index of the codebase. When Lad reviews a pull request, it checks if the new code breaks integrations with other microservices, contradicts established requirements, or violates historical design guidelines.

Problem 3: The "Answer-Only" Training Bias and Lack of Process

To support conversational capabilities, LLMs were overwhelmingly trained on question-answer pairs. They were rarely trained on question-question pairs or question-"I don't know" pairs. Because of this fundamental training bias, LLMs are strongly conditioned to provide an immediate answer, even when a prompt is vague, ambiguous, or contradictory. They almost never stop to ask a clarifying question; instead, they hallucinate a path forward.

In the world of AI coding agents, this is disastrous. It leads agents to jump straight into implementation without clarifying the requirements, silently taking critical design decisions in ambiguous situations without ever confirming them with the user. You might get clean-looking code, but a month later you discover it completely missed the business objective because the agent guessed the requirement instead of asking you.

The Solution: The TDD Skill

To fix this behavior and tie our MCP servers together, we developed the TDD Skill. When appended to a prompt (e.g., Build the new user authentication flow. Follow $tdd.), it overrides the agent's impulse to guess and imposes a strict, disciplined engineering workflow:

1. Write requirements and design specifications first: The agent must draft a permanent REQUIREMENTS.md file. It is explicitly instructed to ask clarifying questions and confirm critical design decisions with the human user before moving forward. The architectural plan is then submitted to Lad for a system_design_review by other LLMs.
2. Follow Test-Driven Development (TDD): The agent must implement tests first, before writing a single line of feature code.
3. Peer Review: The agent submits both the tests and the feature implementations to Lad for a rigorous code review by other LLMs.

Elevating the Abstraction Level of Software Engineering

The combination of Kindly Web Search, Lad Code Review, Serena, and the TDD Skill does more than just fix bugs. It profoundly stabilizes your AI coding agents, making their output reliable, predictable, and fully documented.

For engineers, engineering managers, and tech executives, the business value is clear. This suite allows your team to stop acting as syntax-checkers for AI. It moves the human developer's role from "Writer" to "Architect/Reviewer," allowing you to operate at a consistently higher level of abstraction. You can finally focus entirely on business logic, feature requirements, and user experience, trusting that your AI suite has the context, the up-to-date knowledge, and the rigorous peer-review processes required to execute the implementation flawlessly.