Runbot’s AI Agent Optimiser: The Game-Changer in Conversational Bot Development

Runbot just dropped a bomb on the chatbot industry—and legacy developers are scrambling to keep up. Their new AI Agent Optimiser isn't just another tool—it's a full-stack revolution in bot creation.

How it works: The system uses generative AI to automate what used to take teams weeks—coding dialogue trees, debugging response logic, optimizing user flows. Early tests show it cuts development time by 60% while improving engagement metrics. (Take that, human engineers.)

The finance angle: Of course VCs are frothing—another 'AI' play to justify those bloated valuations. But here's the kicker: Runbot's actually monetizing through enterprise contracts while competitors burn cash on free-tier users.

Bottom line: This isn't incremental improvement—it's existential threat to traditional bot-building. The question isn't whether to adopt, but how fast you can pivot before getting automated into obsolescence.

In Brief

• The AI Agent Optimiser transforms bot optimization into a simple conversation, requiring no technical skills.
• It automatically analyzes strategies and suggests precise adjustments to improve performance.
• Accessible to all, it enables the creation of high-performance, monetizable bots in just a few minutes.

Artificial Intelligence Serving Real Obstacles

Building a bot is one thing. Optimizing it is another. In traditional no-code tools, designing a basic strategy remains accessible. But improving it, increasing its yield, reducing drawdown, or adapting it to the market requires time, trials, and a lot of intuition. Three obstacles hinder the majority of users:

• Lack of time: manually adjusting each parameter takes hours;
• Lack of expertise: it is difficult to know what truly matters;
• Human biases: decisions often rely more on instinct than on data.

Theprovides a clear solution: a smart co-pilot that reads your strategy, understands your goals, and offers targeted improvements in natural language.

Introduction to Runbot: A Complete No-Code Platform

Before diving into the details of the AI Agent, let’s recall whatis. It is a no-code platform dedicated to the creation, testing, and monetization of crypto trading bots. Each strategy becomes anthat can be rented or sold on the integrated.

Runbot offers powerful and intuitive tools:

• Composer: build your bot with sliders and simple rules;
• Optimizer: manually adjust its settings;
• AI Agent: automatically optimize strategies via a conversation;
• Execution via webhooks: secure and non-custodial;
• Marketplace: generate passive income by monetizing your bots.

Its goal is toby combining AI, blockchain, and an intuitive interface.

How Does the AI Agent Optimiser Work?

Once the strategy is created and backtested, the user can activate thefrom the editor. A chat window opens. From there, the exchange begins.

Example: “I want to reduce drawdown” or “Improve profitability“.

It reviews indicators, triggers, timeframes, leverage levels…

The AI proposes precise modifications: EMA period, adding an RSI, adjusting stop loss, etc.

The user chooses the optimization that best matches their goals (higher APR, fewer risks, more trades, etc.)

The optimized strategy is tested automatically, and the results are displayed.

This interaction only takes a few minutes, with no technical knowledge needed.

📘 See the complete guide here: AI Agent Optimiser

🎥 Watch the video tutorial here: AI Agent Optimiser Video

Detailed Example: Creating and Improving a Bot with the AI Agent

To fully understand the usefulness of the AI Agent Optimizer, let’s take a concrete case. The idea is to start with a simple strategy, then improve it using the artificial intelligence integrated into the Runbot platform.

Step 1: Configuration

The user opens the Runbot strategy editor and sets up a trading bot using the no-code interface.

They choose a basic indicator to build their strategy:

• Indicator: EMA (Exponential Moving Average)
• Period: 100
• Timeframe: 4 hours

This setup serves as a simple starting point, often used to follow a medium-term trend.

Step 2: Adding the EMA Indicator

The user defines entry and exit rules based on EMA crossovers. They also choose position parameters:

• Entry: the bot opens a long position when the price crosses the EMA upward
• Exit: the position is closed when the price goes back below the EMA
• Leverage: ×3
• Position Type: long only

At this stage, the strategy is ready to be tested.

Step 3: Defining the Leverage

The user chooses a moderate leverage, here x3, suitable for intermediate profiles. 

Step 4: Entry Trigger

The entry trigger is based on a simple logic: follow the upward trend.

If the price touches the EMA from above (touch down) then the bot opens a buy position (long).

Step 5: Exit Rules

To exit a position, the user chooses clear and symmetrical rules: first, if the asset price falls 5% below the entry price, the trade is closed. Second, as soon as the price touches the EMA from below (touch up), if a long is ongoing, it is closed; otherwise, a short is opened. The other take profit rule is the TP ATR.

Step 6: Free Backtest + Stats Reading

The user runs a free backtest directly in the Runbot interface.
The results appear immediately:

• The strategy works, but the drawdown is high
• The success rate remains low
• Gains are irregular, with phases of underperformance

This is where optimization becomes necessary.

Step 7: Discussion with AI to Optimize the Strategy

The user activates theintegrated into the platform. A chat window opens. They state their objectives:

• “Reduce the drawdown”
• “Improve the success rate”

The AI analyzes the existing strategy and suggests modifications:

• Reduce the EMA period (for example from 100 to 50) for more reactive signals
• Add a second indicator such as RSI or Bollinger Bands to filter out false signals
• Implement a stop loss and a take profit, adapted to the observed volatility level

The user validates the suggestions and runs a new test.

Step 8: Results, Comparison: Strategy Improved After Optimization

The second backtest confirms the strategy improvement:

• Drawdown significantly reduced
• Improved gain/loss ratio, with more consistent performance
• More stable equity curve, which strengthens the bot’s reliability over time

In a few minutes and without technical expertise, the user transforms a basic strategy into a high-performing bot, ready to be deployed… or monetized.

Why is it a True Game Changer?

The AI Agent Optimizer does not just assist the user; it profoundly transforms how they create and optimize their bots. Where manual strategy adjustment took hours, a few minutes are now enough to obtain a concrete and relevant result. This time saving redefines the efficiency of the creation process.

Accessibility is another fundamental change.nor DEEP reading of technical indicators. Artificial intelligence interprets objectives expressed in natural language and proposes suitable settings, thereby eliminating technical complexity.

In terms of performance, AI relies on historical data analysis to identify the most effective combinations., improving results while limiting risks. 

This new tool allows for greater: by facilitating the creation of robust strategies, it opens the way to producing several quality bots, usable on the marketplace. Each user can now develop their portfolio of strategies without prior expertise and access new sources of income.

Monetization & Marketplace: Benefits for Creators

Every strategy created on Runbot is automatically turned into an NFT.on the integrated marketplace. The more performant and well-optimized the strategy is, the more it attracts users seeking ready-to-use solutions.

The AI Agent plays a key role here:that makes their bots competitive and attractive. Thanks to this intelligent support, users can consider a real business around strategy design, with potential regular income. This mechanism transforms bot creation into a full-fledged economic model, accessible to the widest audience.

Best Practices and Important Reminders

Even with such a powerful assistant as the AI Agent, some fundamental rules of algorithmic trading must remain at the heart of the approach. A backtest, no matter how successful,. Market conditions evolve, and a strategy that was effective yesterday can become obsolete tomorrow.

It is also important to. By adjusting parameters too finely, one risks producing a fragile strategy, too dependent on the past and poorly adapted to real market fluctuations. To limit this risk, it is recommended to diversify bots. By spreading exposure across multiple strategies, the resilience of the entire portfolio is strengthened.

Finally,remains essential. Defining appropriate position sizes, limiting potential losses, and securing gains should be an integral part of every strategy. Artificial intelligence assists, but it is always the trader who retains control over their decisions.

With the, Runbot takes a new step in democratizing algorithmic trading. Creating, testing, and improving bots becomes simple, fast, and accessible to everyone.

👉 Start your first test on Runbot.io
📘 Access the full guide: AI Agent Optimiser
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