CEO fires his Devid and gives it to LLM instead of work

How my company completed manual coding and built the adult -based development process.

It started with understanding, which at first seemed uncomfortable: despite all the talents of our team, most of them felt what we were building … Repeatable. Predictable. Bored, even.

At first we were a consulting shop. Talented Devid, working on various products, frameworks, business logic. But every time we sent something, I kept noticed that we spent a huge amount of human brain power on tasks that were not really intellectual challenges. They were familiar, reusable and frankly – automatic.

Then I started experimenting with AI agents.

Not assistants. Not “automatic steroids”. Agents.

What do we mean under “agents”

For us, the AI ​​agent is a role -based autonomic or semi -autonomous process fed by a large language model that will receive:

• interpret the purpose,
• Divide it into sub -race,
• cause through restrictions,
• Call tools (eg code generation, test runners, API validators),
• and communicate with other agents or people.

Think about the DEV team made from LLMS. Everyone has a job. Each of them is quickly designed to act within the guards. And each communicates through structured memory or logs.

We didn't get here overnight. Here's how we did the switch.

Step 1: Repeated recognition of codes

When we built Linkeme.AI -Luxembourg AI tool for automating social media content -we hit the wall. We built the third administrator's dashboard for two months. CRUD logic. Swallowing of the data. Analytics display. Some role -based access control. A couple of API integration. Nothing we had done a dozen.

And yet, it had eaten our time.

So we organized an experiment. We took the module we had to build – the reporting dashboard – and asked for the entire background program to generate the entire background program based on Claude 3.5. Then we asked Deepsek to generate a test area. Then to write a GPT-4 API DOC.

The result was not perfect. But it was functional. About 70% of the way there. This was enough to confirm the bet: AI could replace DEV for usTo.

Step 2: Building a stack of our agent

We did not want a monolithic wrap around GPT. We wanted modularity. So we built what we now call our “agent's backbone”: a set of compostable substances, each with a clear task and identity.

• Orchestrator: Takes the specification and creates sub -tasks
• Rear builder: Handles logic, structure, services
• Qa agent: Triggers a test coverage, an edge wrapping
• Scout: Reviews of attack surfaces and authentic
• API synthesis: Generates documents compatible with Openap
• Human pledge: Checkpoint, not formality

We also use tools such as bolt.new for initial scaffolding, to start the internal agent of the line vs. code and Claude or Deepseek depending on the type of task.

All of these substances work through a shared memory layer (we use a lightweight radish-based scratch plate) and log our decisions to log. This audience was the key to the team's outsourcing.

To ensure the smooth communication and context awareness of agents and tasks, we introduced MCPs (model context protocols) – structured templates that define context division, what data persistent between agents, and how the logic of withdrawal is treated. MCP helps reduce hallucinations, enforce consistency and improve multi-stage reliability.

Step 3: Team transition and cultural shift

When I announced that we would stop writing the code by hand, I waited for a setback. What I got was curiosity.

Dev after Dev asked, “Be it … but how do I talk to the agents?”

The shift was surprisingly smooth. The engineers became an agent Wranglers. They learned to write better data, make better instructions and interpret AI output. Instead of waste of spiritual energy on the syntax, they focused on architecture and integration.

Our junior Devid received QA strategists. Our leading background DEV now manages the quick design of the agent. Our product people got better in writing restrictions. We didn't let anyone down. We leveled the team.

And yes – let's still write the code when it matters. But never twice.

An overview of the architecture

You can see this architecture through the dashboard of our inner visualization, which we are going to open soon. Start everything through a private DEV orchestra layer, which is a container in Docker. Each agent works in the context of its service. We use LLM APIs (Anthropic, Openai, Deepseek) and a small router layer selects model latency times, context size or type of task.

Agents are stateless, but the context of the project is stored and injected according to the task. We also built the dashboard of the user interface:

• See agents logs
• To rename the tasks
• Try the steps with the new signposts again
• The flag of weird outputs

We use a posthog agent to monitor the quality of the response and measure the frequency of human defeat.

Results (and challenges)

We measured several things in three months:

• Time to the first function used: less than 65%
• Internal errors per edition: less than 40%
• Number of manual lines: less than 80%
• Time spent to argue over the code style: zero

But we also caught the challenges. The quick drift is real. Hallucinations still happen. Agents sometimes forget the state when the context is too large. And if something goes wrong, smoothing the logic of a multi -agent differs from a very smoothing code.

We have had to build internal tools to monitor the behavior of the agent. And the team's training took time to get fast architecture. But none of them outweighed the speed and the focus we got.

What comes next

Easylab AI is no longer a DEV agency. We are an AI orchestration company. We help others do what we did: replace repeated workflows with structured AI agents that carry out, adjust and learn.

And we are constantly improving our system. Next: Memory -based representatives who refine themselves by projects. Context Chain. Long -term discussion. And finally, the meta agents that make up the sub -communities.

This is not the future. It works now. We did not replace the developers. We made them 10x more valuable.

And yes, I still get messages like:

“Did you really let your Devid AI away?”

My answer? Only those who wanted to write the same button logic for the fifth time.