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18/08/2026 3:01 pm
TL;DR: AI agent framework choice in 2026 is increasingly about orchestration fit, but Braintrust’s analysis shows the harder problem is portable evaluation across LangGraph, CrewAI, OpenAI Agents SDK, Mastra, and LlamaIndex, because each emits different trace structures and failure modes. Without a framework-agnostic eval layer, teams will ship inconsistent quality controls as agent stacks evolve.
NHIMG editorial — based on content published by Braintrust: Best AI agent frameworks (2026): How to choose one and add evals
Questions worth separating out
Q: How should security teams govern AI agents that reason across multiple data platforms?
A: Security teams should govern the meaning layer, not just the access layer.
Q: Why do local AI agents complicate identity and access management?
A: They can retain legitimate permissions while changing timing, prioritisation, and action sequence outside human presence.
Q: What breaks when evaluation is tied to a single AI agent framework?
A: Portability breaks first, then consistency.
Practitioner guidance
- Define the orchestration pattern before picking a framework Map whether the agent needs branching, retries, human approval gates, multi-agent task passing, or retrieval-heavy reasoning.
- Build a framework-agnostic evaluation layer Capture framework-specific traces into a shared evaluation pipeline so retrieval quality, tool-call correctness, routing accuracy, and final output quality can be scored consistently across stacks.
- Tie agent tool access to identity governance Treat every connected tool, API, and data source as delegated access.
What's in the full article
Braintrust's full article covers the implementation detail this post intentionally leaves for the source:
- Framework-by-framework comparison of trace structures and integration patterns
- Code examples for capturing spans from LangGraph, CrewAI, OpenAI Agents SDK, Mastra, and LlamaIndex
- Practical eval scoring ideas for retrieval quality, handoff routing, and tool-call accuracy
- Migration guidance for teams running mixed agent stacks in production
👉 Read Braintrust's analysis of the best AI agent frameworks and evals for 2026 →
AI agent framework choice is only half the production problem?
Explore further
18/08/2026 3:40 pm
Portable evaluation is now a governance requirement, not an engineering nice-to-have. When agent frameworks generate different traces, the organisation loses consistent evidence unless the eval layer sits above the orchestration stack. That means governance teams need a control plane for outcomes, not just a framework decision for development convenience. For identity programmes, this is the same lesson as centralising audit for distributed access paths.
A question worth separating out:
Q: Who should own AI agent risk when an agent can use business tools?
A: Ownership should sit across AI security, IAM, and the business team that granted the tool access. The model team owns safety testing, IAM owns entitlement scope, and the system owner owns the business impact. If one group owns only the model, the organisation misses the access-control risk.
👉 Read our full editorial: AI agent frameworks need portable evals before production scale
