The Training Platform for Reliable AI Agents
Parameterize your agentic systems. Improve them with
simulations — without tuning model weights.
Backed by
AI Agent
System Prompt
You are a helpful AI assistant trained to provide accurate and thoughtful responses. You approach each question with care, considering context and nuance. Your goal ...
Tool Call Descriptions
search_web(query="latest AI research") read_file(path="/data/context.txt") write_file(path="/output/result.json", content="{...}") execute_code(language="python", code="import numpy...") call_api(endpoint="https://api.example.com/v1/...", method="POST")
Tools
Search
Database
Code
Tools
Guardrails
CRITICAL SAFETY RULES: - Never execute destructive commands without confirmation - Validate all user inputs for SQL injection patterns - Rate limit: max 100 requests per minute - No access to production databases - Require authentication for sensitive operations - Log all security-relevant events
Policies
BEHAVIOR POLICIES: - Maintain professional, helpful tone - Cite sources when providing factual information - Admit uncertainty rather than guess - Respect user privacy - no data retention - Follow ethical AI guidelines - Provide explanations for complex decisions
Context
CURRENT CONTEXT: User: Data Scientist Task: Analyze sales trends Dataset: Q4_2024_sales.csv Environment: Production Timestamp: 2024-01-16T14:30:00Z Session ID: a8f3d2c1-b4e5-4f6a-9d7e-1c2b3a4d5e6f Previous queries: 3
Memory
SHORT-TERM MEMORY: - User asked about sales trends 2 minutes ago - Loaded dataset: 45,000 rows - Identified top product: Widget Pro X - Found anomaly in December data LONG-TERM MEMORY: - User preferences: Python over R - Common visualizations: bar charts - Typical workflow: clean → analyze → visualize
Models
GPT-4
Claude
Gemini
Llama
Initializing Training...
Trusted and supported by engineers at
Our Philosophy
There's a better way to improve AI Agents
Today, most AI agent development still relies on manual trial and error.
We're building the future where AI Agents automatically improve, learn, and train in the background—not as black boxes, but through visible changes you can see and understand. We'd love to have you join us.
How It Works
PHASE 1
Parameterize your Agentic Systems
Start by choosing which parts of your agent you want to expose. This can include the LLM, memory, system prompts, context, tool management, guardrails, and more.
If you currently "tweak settings until it works," those are exactly the parameters we help you formalize and optimize.
Parameters
Results
Logs
Agent Parameters
Auto-tune
Memory
System Prompt
Models
Context
Tools
Guardrails
PHASE 2
Run Targeted Simulations
Agent behavior depends on many interacting components. Lucidic runs controlled simulations that systematically vary design choices to learn the behavior space.
This includes varying prompts, tools, policies, and configurations in isolation and in combination, and learning how these choices relate to similar and different test cases
Simulations
History
Settings
Decision Tree
Exploring optimal paths
Live
PHASE 3
Make Your Agents Better
Our algorithms combine ideas from genetic algorithms and reinforcement learning to systematically search for the optimal configurations of parameters for your AI agents.
The optimization is driven by your custom evaluations and datasets, ensuring the resulting agent improvements stay aligned with your goals and metrics.
Metrics
Charts
Export
Performance Metrics
Real-time analytics
Auto-refresh
Overview
Accuracy
Variance
Iterations
Support
Deflect Rate
Resolve Rate
Fake Deflect
Better Than State of the Art
Benchmarks
Up to 10x Better than the State of the Art
Lucidic's optimization algorithms consistently outperform state-of-the-art prompt engineering frameworks like DSPy across multiple benchmarks. Our automated approach achieves up to 10x better results on complex reasoning tasks, while requiring no manual prompt tuning. These benchmarks include multi-hop question answering, instruction following, and structured output generation.
Lucidic AI
44.5%
DSPy GEPA
4.7%
Anthropic
2%
OpenAI
2%
Baseline
2%
*All experiments were conducted with standardized metrics. Model: GPT-4.1 Mini
Case Studies
Real results from agentic training
Customer satisfaction rate was a crucial metric that was dangerously low, but after using Lucidic's auto-improvement algorithms, it improved significantly.
6 months
Time saved by using Lucidic's auto-improvement algorithms
48%
Relative improvement from customer resolution rate baseline
Cresta Case Study
Read morePlatform Features
Everything you need to train your agentic systems
Works with your stack
Agent Integration
Integrate with any LLM provider and agent framework. LangChain, LangGraph, Langfuse, OpenAI, Anthropic — Lucidic works with what you already use.
Define success
Custom Reward Definition
Define objective functions aligned with domain-specific metrics — inference latency, computational cost, and other measurable outcomes.
Explore possibilities
Intelligent Candidate Exploration
Automatically search thousands of agent configurations — prompt variants, tool orderings, context strategies — to find what works best.
Ship safely
Continuous Improvement in Production
Deploy improved agents with controlled rollouts. Gradually shift traffic, monitor for regressions, auto-promote or rollback.
Dive into Research
Explore Our Latest Research
Discover insights from our ongoing research into AI agent optimization, performance analysis, and evaluation methodologies.
View Research
Applications of Training
We help teams solve the hardest challenges in agentic deployment
Train against outcomes that matter
Optimize Your Metrics
Train agents on what matters in production: accuracy, CSAT, resolution rate, escalation rate, and more. Lucidic optimizes toward the metrics that define success for your product.
Testimonials
Trusted by engineers building the next generation of AI agents
We were running agents one by one, but with Lucidic's simulations we can run hundreds in parallel, testing 10x faster than we used to be able to.
I can't vouch for the Lucidic team enough. They will help you at every step of the way and at every hour of the day.
I would recommend Lucidic to any developer who wants to build reliable AI agents fast.
We were running agents one by one, but with Lucidic's simulations we can run hundreds in parallel, testing 10x faster than we used to be able to.
I can't vouch for the Lucidic team enough. They will help you at every step of the way and at every hour of the day.
I would recommend Lucidic to any developer who wants to build reliable AI agents fast.
We were running agents one by one, but with Lucidic's simulations we can run hundreds in parallel, testing 10x faster than we used to be able to.
I can't vouch for the Lucidic team enough. They will help you at every step of the way and at every hour of the day.
I would recommend Lucidic to any developer who wants to build reliable AI agents fast.
Making progress on our agent sped up 5x from using their datasets and auto-improvement feature to test dozens of configurations on our agent.
Evaluations were a big pain point for us, and Lucidic helped us turn them into an automated process. Iterating on our agent was way easier.
Lucidic helped me turn my disorganized runs into a repeatable process. Each cycle made the agent stronger and more robust.
Making progress on our agent sped up 5x from using their datasets and auto-improvement feature to test dozens of configurations on our agent.
Evaluations were a big pain point for us, and Lucidic helped us turn them into an automated process. Iterating on our agent was way easier.
Lucidic helped me turn my disorganized runs into a repeatable process. Each cycle made the agent stronger and more robust.
Making progress on our agent sped up 5x from using their datasets and auto-improvement feature to test dozens of configurations on our agent.
Evaluations were a big pain point for us, and Lucidic helped us turn them into an automated process. Iterating on our agent was way easier.
Lucidic helped me turn my disorganized runs into a repeatable process. Each cycle made the agent stronger and more robust.
Frequently Asked Questions
Who should use Lucidic?
Lucidic is for teams that want more reliable, higher-performing AI agents. Whether you fine-tune models or rely on off-the-shelf LLMs, Lucidic helps teams systematically all parameters of the agent without manual iteration.
Does Lucidic AI build agents?
No. We don’t replace your agent architecture or infrastructure. We optimize and train the agents you already have, focusing on reliability, accuracy, and continuous improvement.
How does the training process work?
Lucidic runs structured simulations that systematically vary agent design choices across scenarios. Performance is evaluated against your chosen metrics, and learning algorithms—including evolutionary methods, reinforcement learning, and Bayesian optimization—use this signal to iteratively improve agent behavior over time.
Do I need to rebuild my agent to use Lucidic?
No, Lucidic integrates with all major LLM providers and agent frameworks. We don’t rebuild or replace your agents—instead, we train and improve the components you already use, including prompts, tools, policies, and decision logic.
What types of AI agents can be trained?
Lucidic works with any LLM-powered agent including customer support agents, coding assistants, data analysis tools, and custom enterprise agents. If it uses an LLM, we can optimize it.
Do you need a training environment and reward to use Lucidic?
No—Lucidic can work with what you already have. If a training environment or reward signal isn’t fully defined, our team can help design and refine them as part of the engagement. Clearer environments and richer signals enable stronger learning, and we work with customers to progressively build those pieces over time.
%20(2).DYlGygaK.png)