Automate Secure Enterprise Code
This challenge focuses on building a sophisticated multi-agent system for secure, automated software development within an enterprise environment. Participants will architect a team of specialized agents using AutoGen to collaborate on a given software development task, from requirements gathering to code generation, testing, and deployment. The core innovation lies in integrating these agents with enterprise systems (e.g., version control, CI/CD, internal knowledge bases) through the MCP. MCP will ensure secure, audited, and controlled access for agents to sensitive enterprise tools and data. GPT-5 will serve as the primary reasoning engine for complex code generation and architectural decisions, employing extended thinking and adaptive reasoning budgets to tackle intricate problems. This challenge emphasizes production-ready agent deployment, security, and seamless integration into existing enterprise workflows.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge focuses on building a sophisticated multi-agent system for secure, automated software development within an enterprise environment. Participants will architect a team of specialized agents using AutoGen to collaborate on a given software development task, from requirements gathering to code generation, testing, and deployment. The core innovation lies in integrating these agents with enterprise systems (e.g., version control, CI/CD, internal knowledge bases) through the MCP. MCP will ensure secure, audited, and controlled access for agents to sensitive enterprise tools and data. GPT-5 will serve as the primary reasoning engine for complex code generation and architectural decisions, employing extended thinking and adaptive reasoning budgets to tackle intricate problems. This challenge emphasizes production-ready agent deployment, security, and seamless integration into existing enterprise workflows.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master AutoGen for orchestrating collaborative agent teams with defined roles (e.g., 'Architect', 'Developer', 'Tester').
Implement Model Context Protocol (Model Context Protocol) for secure, controlled, and auditable access to enterprise APIs and data sources.
Build extended thinking pipelines with GPT-5, enabling agents to break down complex problems and iterate on solutions.
Deploy adaptive reasoning budgets for agents, allowing dynamic allocation of computational resources based on task complexity.
Design RAG systems to provide agents with up-to-date documentation, best practices, and enterprise-specific knowledge.
Integrate OpenAI o3 for specialized tool functions within the AutoGen framework, enhancing agent capabilities beyond raw LLM output.
How this agent runs
The evaluation will assess the completeness, correctness, and security of the generated code, as well as the agents' ability to effectively collaborate and securely interact with simulated enterprise systems via Model...
Challenge input
{ 'feature_request': 'Develop a Python function to securely hash user passwords using a specified algorithm and salt.' }
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{ 'generated_code': 'def hash_password(...)', 'unit_tests': 'import unittest...', 'repository_update_log': ['git add...', 'git commit...'] }
- Unit tests pass for the generated Python function.
- Demonstrate that critical enterprise interactions (e.g., repository access, secret retrieval) are routed th...
- The generated code adheres to basic security best practices (e.g., no hardcoded secrets, proper hashing).
- Code Completeness target: 0.9
- Docker execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
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