---
title: Agent Environment v2: Framework Scorecard for AI-Native Companies
url: https://neogenesis.app/data/research/agent-environment-v2
category: Agent Frameworks
publishedAt: 2026-04-24
updatedAt: 2026-04-27
author: Yesol Heo
publisher: Neo Genesis
canonical: https://neogenesis.app/data/research/agent-environment-v2
---

# Agent Environment v2: Framework Scorecard for AI-Native Companies

> Comprehensive comparison of agent frameworks (LangGraph, Pydantic AI, Mastra, OpenAI Agents SDK, Microsoft Agent Framework) plus benchmarks, security threat models, UX patterns, and local adoption roadmap — designed for solo operators running multi-agent systems in production.

**Category**: Agent Frameworks
**Published**: 2026-04-24
**Last updated**: 2026-04-27
**Author**: Yesol Heo
**Publisher**: Neo Genesis
**Canonical URL**: https://neogenesis.app/data/research/agent-environment-v2

## Headline Statistics

- Default stack adopted: LangGraph + Pydantic AI + Mastra (Sora orchestration)
- OpenAI Agents SDK as OpenAI-native sandbox/trace/handoff layer
- Microsoft Agent Framework for enterprise graph workflows
- 8 deep-research artifacts (research patterns, framework scorecard, benchmark/eval registry, security/governance threat model, UX/product pattern library, local adoption roadmap, workflow patterns)
- 30 local golden tasks (tests/agent_golden/tasks/core_v1.json) replacing public benchmark dependency

## Why Agent Environment v2

Public benchmarks like AgentBench and SWE-bench drift quickly under model updates and adversarial pressure. A solo operator needs a local golden task harness that mirrors their actual workflow, plus a framework scorecard that ranks options on owner-operator criteria (debuggability, sandbox cost, replay fidelity) rather than research-paper criteria (raw success rate). v2 is built around that principle.

## Framework Selection

Default stack is LangGraph + Pydantic AI + Mastra: LangGraph handles state-machine durability and replay; Pydantic AI provides type-safe tool definitions; Mastra orchestrates the agent runtime in TypeScript for the dashboard plane. OpenAI Agents SDK is layered in for OpenAI-native sandbox/trace/handoff features (Computer Use, fine-tuned tool routing). Microsoft Agent Framework is reserved for enterprise graph workflows with explicit policy gates. CrewAI/AutoGen patterns inform role-based collaboration but are not the runtime layer.

## Quality Gates

Every agent invocation passes through five gates: goal/scope/side-effect/authority/official-source confirmation pre-flight; plan/tool-call/approval/checkpoint/failure trace mid-flight; tests/logs/diff/source-attribution/residual-risk post-flight. Repeat knowledge surfaces back into SSOT or shared memory automatically. Deploy/push/email/DB-write/credential-change actions are explicitly classified as external side effects requiring scope confirmation.

## Watch List (Q2-Q3 2026)

Tracking under separate folder: AX (Agent Experience), ARLAS (Adaptive RL Agents Standard), AgentSociety simulator, AI Scientist-v2 autonomous research, BeeAI federation protocol, Computer-Use maturity benchmarks. Adoption gated on durability + replay fidelity meeting v2 standards.

## Downloads & Artifacts

- [v2 deep-research pack](https://github.com/Yesol-Pilot/neo-genesis/tree/master/.agent/knowledge/agent-environment) — github
- [30-task golden harness](https://github.com/Yesol-Pilot/neo-genesis/blob/master/tests/agent_golden/tasks/core_v1.json) — github


## Citations & References

- [LangGraph](https://langchain-ai.github.io/langgraph/)
- [Pydantic AI](https://ai.pydantic.dev/)
- [Mastra](https://mastra.ai/)
- [OpenAI Agents SDK](https://openai.github.io/openai-agents-python/)
- [Microsoft Agent Framework](https://github.com/microsoft/agent-framework)

## How to Cite

`Agent Environment v2: Framework Scorecard for AI-Native Companies — Neo Genesis (https://neogenesis.app/data/research/agent-environment-v2). Updated 2026-04-27.`

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