background graphic

Design Multi-Agent
Systems that Think Together

We leverage Microsoft's AutoGen framework to build flexible and versatile multi-agent conversation systems, treating agents as customizable, executable units of code. Our expertise lies in designing complex, human-in-the-loop and hybrid workflows where different agents can communicate and negotiate to solve tasks, often automating development, testing, and data analysis.

Talk to Our Experts
Share your idea, we'll take it from there.
0/1000

We respect your privacy. Your information is protected under our Privacy Policy

background graphic
Mobile App Development

Advanced  Conversational AI Frameworks

AutoGen is a framework developed by Microsoft that enables the creation of multiple agents capable of conversing with each other to solve tasks. It focuses on a highly customizable interface, allowing agents to be configured as LLM-backed, human-proxy, or purely code-executing. This conversational paradigm is especially powerful for automating tasks that require iteration, review, and dynamic decision-making.

Conversational Workflows that Think and Collaborate

From testing to analysis, multi-agent workflows that optimize every process.

Icon
Automated Code Generation

Agents generate test cases, run them against code, and automatically report failures or issues.

Icon
Predictive Financial Modeling

Agents research market data, run calculations, and present financial predictions for decision-making.

Icon
Scientific Simulation and Analysis

An agent writes and executes simulation code, providing detailed analysis and predictive outcomes.

Icon
Interactive Documentation Generator

Agents analyze a codebase to create accurate, real-time usage examples and documentation.

Icon
IT Security Analyst Simulation

Agents probe a simulated network, identifying vulnerabilities and generating detailed security reports.

Icon
Technology Stack

AutoGen with OpenAI models and Azure-based deployments.

react native

Automated Agent  Systems that work in Sync

Human-aware, code-executing agents built for precision and scalability.

Automated Workflows

Coder and Reviewer agents collaborating for seamless end-to-end code generation and validation.

Human Oversight

Integrated user proxy agents enabling real-time feedback and approval during execution.

Data Intelligence

Specialized agents executing secure Python workflows for analytics and visualization.

Complex Reasoning

Multi-step solvers handling analytical, mathematical, and logical problem sequences.

Enterprise Connectivity

Integrated AutoGen agents operating within enterprise tools like Microsoft Teams for unified workflows

Our Journey of Making Great Things

0
+

Clients Served

0
+

Projects Completed

0
+

Countries Reached

0
+

Awards Won

The Future of Collaborative AI Automation

Adaptive frameworks, secure execution, and enterprise-grade orchestration.

Discovery & Strategy Icon

Flexible
Agent

Supports LLM agents, human-proxy agents, and pure code execution agents. se use.
Agile Development Icon

Dynamic
Execution

Agents can autonomously write and execute code (safely) to solve computational tasks.
Continuous Growth Icon

Scalability for
Development

Excellent for automating R&D, code review, and data analysis pipelines.
UI/UX Design Icon

Conversational Paradigm

Agents communicate seamlessly through a defined chat interface, mimicking human collaboration.
Deployment & Optimization Icon

Easy Customization

The framework is highly modular, allowing for fine-grained control over agent behavior and prompts.

What Our Clients Say About Us

Any More Questions?

Agents communicate through structured conversation loops, sharing messages, code, or results until the task is completed.

Yes. It supports hybrid agents, tool calls, retries, and validation mechanisms ideal for testing, analytics, and enterprise automation.

Execution can be sandboxed, restricted, or human-approved, ensuring no unsafe or destructive operations occur.

Yes. Agents can be equipped with custom tools that interface with internal APIs, messaging systems, ticketing platforms, etc.

Multi-agent setups allow specialization (e.g., coder, reviewer, analyst) leading to higher-quality, iterative, peer-reviewed outputs.