In recent months, one idea keeps coming up on developer social networks and forums: AI is supposedly already better at coding than most software engineers. Whether it's generating code-explained videos with anything2explainer, creating interactive 3D scenes through sub-agents like dream-loop, or writing complex scripts in seconds, automating code production is no longer science fiction.
However, experienced developers know it well: writing raw syntax has never been the most critical or the hardest part of the job. The real challenge lies in data reliability, managing interface contracts (APIs), robust debugging and maintaining architectures.
Looking at current trends, one thing is clear: the more we delegate script writing to LLMs and autonomous agents, the more we handle huge volumes of structured data, mostly as JSON payloads. And that's often where the chain breaks.
The proliferation of AI-generated JSON payloads
Modern AI-based architectures work almost exclusively by exchanging structured data. When an agent like Claude Code or GPT-4o communicates with a third-party tool or an external API (function calling, tool use), calls and responses systematically travel as JSON structures.
Here's what concretely happens in a modern automated workflow:
- The user enters a complex instruction.
- The AI plans its actions and generates a request as a JSON object.
- The target API processes this object and returns a result as a minified JSON payload or a verbose stream.
- A sub-agent analyzes this response to decide what to do next.
On paper, everything looks perfectly smooth. But in practice, while developing and debugging these agents, you find yourself flooded with endless JSON strings, compacted on a single line, sometimes containing subtle syntax errors, badly escaped quotes or inconsistent types.
To keep control of your application and understand exactly what your agent sends and receives, immediate visual inspection is irreplaceable.
Data inspection: the human developer's strong link
Even if a language model can write a perfect TypeScript function in two seconds, it won't replace the rigor of a developer checking that an API contract is respected in a staging environment.
Faced with an incomprehensible error log from an AI agent pipeline, the first step is always to isolate the faulty JSON stream:
- Does the returned structure match the expected schema?
- Is there a missing key or an unexpected
nullfield? - Is the JSON even syntactically valid?
Trying to read a 10 KB minified payload in a terminal console or in CloudWatch logs is a monumental waste of time. It's precisely to address this everyday problem that specialized tools for instant formatting and validation become essential in a developer's daily work.
To quickly clean up and analyze your API responses or AI-generated configurations, you can use JSON Formatter & Validator, a free and instant tool available on JcHub. It lets you format, minify and check the validity of your JSON files in one click, right in the browser and without sending your sensitive data to a third-party server.
How to build JSON validation into your automated workflows
To avoid the frequent crashes that come with integrating AI-generated output, here are three best practices to adopt right away in your projects:
1. Never trust the LLM's raw formatting
Even when configured to answer strictly in JSON (response_format: { type: "json_object" }), a model can sometimes add stray text around the answer or produce invalid syntax if the maximum number of tokens is reached during the stream.
2. Validate the schema on input and output
Use lightweight schema validation libraries (like Zod or TypeBox in TypeScript, or Pydantic in Python). Before sending the AI-generated payload to your database or to a critical API, run the JSON through a strict validation step.
3. Keep a quick formatting tool at hand
During local development, get into the habit of pasting your complex payloads into a viewer. Spotting wrong indentation or a badly structured array takes 2 seconds with a good visual tool, while hunting for the cause in an obscure stack trace can take an hour.
Conclusion: AI augments developers, it doesn't replace them
Yes, AI codes faster than most developers when it comes to writing boilerplate or turning an idea into a raw algorithm. But the real value of a software engineer lies in overall mastery of code quality, data security and understanding network exchanges.
By equipping yourself with the right lightweight, effective tools to inspect and validate your structured data, you save precious time and focus on what matters: building value and solid architectures.