Stop Picking Agent Frameworks by Features (2026)

Stop Picking Agent Frameworks by Features

TL;DR

The LangChain vs. CrewAI vs. AutoGen debate misses the point. Framework features don't kill enterprise agents. Lack of replayable traces, eval gates, and audit logs does. We've seen teams cut agent debugging time from 30 minutes to 5 minutes per incident just by adding proper AI agent observability. That's 41+ hours and $6,250 saved every month on a 10-agent workflow. Pick your stack by debuggability, not multi-agent hype.

Why Everyone's Asking the Wrong Question

Every week someone asks me which agent framework to use. LangGraph or CrewAI? AutoGen or Semantic Kernel? They've read the comparison posts. They've watched the YouTube demos.

They're asking the wrong question.

We've built over 100 AI automations at StoryPros. The pattern is always the same. The framework doesn't matter nearly as much as whether you can figure out what went wrong at 2 AM on a Tuesday.

Klarna runs its AI assistant on LangGraph and LangSmith, handling 2.5 million conversations for 85 million users. LinkedIn, Uber, and Replit all chose LangGraph too. But they didn't pick it for the multi-agent features. They picked it because LangSmith gives them traces they can actually read.

AI agent observability means seeing every step an agent takes. Every LLM call. Every tool invocation. Every decision point. Without it, you're flying blind in production.

The Debug Tax Is Costing You $75K a Year

Here's the math nobody's doing.

Say you run a 10-agent workflow. Tool calls fail about 2% of the time. That's a reasonable failure rate based on what we see in production. That gives you roughly 100 incidents a month.

Without traces, each incident takes your engineer about 30 minutes to diagnose. They're scrolling through logs, guessing which agent made the bad call, trying to reproduce the issue.

Incident.io's data shows teams waste 15+ minutes per incident just on coordination overhead before troubleshooting even starts. They call this the "coordination tax." It eats up to 25% of your total resolution time.

With proper traces, that same incident takes 5 minutes. You pull up the trace ID, see the exact span where the tool call failed, read the input and output, and fix it.

That's 25 minutes saved per incident. At 100 incidents a month, that's 41.7 hours. At $150/hour for a senior engineer, that's $6,250 per month. Over $75,000 a year. On one workflow.

AgentixLabs documented a case where a lead research agent looked perfect in demos but stalled on 8% of accounts in production. One slow API was the culprit. Without step-level tracing, the team would've spent days guessing. With it, they found the bottleneck in minutes.

Incident.io customers report 37% faster resolution times after adding automated tracing and post-mortems. That number tracks with what we see when we add observability to agent deployments.

What You Actually Need: Traces, Evals, and Audit Logs

Forget feature matrices. Here are the three things that determine whether your agents survive production.

Replayable traces. Every agent run needs a single trace ID that connects every step. Every LLM call, tool execution, and decision point gets its own span. OpenTelemetry is the standard here.

AG2 (formerly AutoGen) now has built-in OpenTelemetry tracing. It captures every conversation turn, LLM call, tool execution, and speaker selection as a structured span. You can export to Jaeger, Grafana Tempo, Datadog, or Honeycomb.

LangChain has LangSmith. If your framework doesn't give you this, build it yourself with OpenTelemetry's GenAI Semantic Conventions.

Eval gates. These are automated quality checks that run before an agent's output reaches a customer or triggers a downstream action. Think of them like CI/CD tests for your agents.

Promptfoo lets you define assertions against agent outputs. You can check for hallucinations, PII leakage, or just plain wrong answers. Run these in your deployment pipeline. If evals fail, the deploy stops.

We run evals on every agent before it touches production. No exceptions.

Audit logs. Every action an agent takes needs a timestamped, immutable record. What input did it get? What did it decide? What tool did it call? What was the result?

This isn't optional for regulated industries. But even if you're not in fintech or healthcare, audit logs save you when a client asks "why did your AI send that email?" You need to answer in 60 seconds, not 60 hours.

How to Pick Your Stack (A Decision Framework)

Stop comparing framework features side by side. Start asking these four questions.

Can I get a full trace of any agent run in under 30 seconds?

LangSmith does this natively for LangGraph. AG2 does it with OpenTelemetry. CrewAI requires you to bolt on your own tracing. If the answer is "we'd need to build that," add 2-4 weeks to your timeline and $10K-$20K to your budget.

Can I replay a failed run with the same inputs?

This is where most frameworks fall apart. One developer documented 8 distinct failure modes building a dual-orchestrator system on LangGraph: context compression causing amnesia, one model failing to catch its own mistakes in code review, crashes losing state.

Deterministic replay requires capturing every input, every random seed, every external API response. OpenTelemetry gives you the structure. You still need to store the payloads.

Can I block a bad output before it ships?

This means eval gates in your pipeline. Promptfoo, Ragas, or custom validators using PydanticAI. The eval runs against the agent's output. If it fails a threshold, the action doesn't execute. Your agent doesn't book a meeting with the wrong person. It doesn't send a refund to the wrong account.

Can my compliance team audit any agent decision?

If you're selling to enterprise, this isn't negotiable. Your audit logs need to be PII-safe. That means redacting sensitive data before storage but keeping enough context to reconstruct the decision chain. OpenTelemetry's attribute system lets you tag spans with custom metadata while stripping PII at the collector level.

I'll say it plainly: the "best" framework is the one you can debug at 2 AM. Everything else is marketing.

The Boring Stack That Works

Here's what we actually deploy at StoryPros for clients who need agents in production.

OpenTelemetry for tracing. It's vendor-neutral. You're not locked into LangSmith or any single backend. You can send traces to Datadog, Honeycomb, Grafana, or all three. The Spanora guide puts it well: "OTEL traces are vendor-neutral. You can switch observability backends without re-instrumenting your code."

n8n for orchestration. We use it instead of Zapier because it gives us full control over the workflow logic and we can instrument every node.

Promptfoo for evals. It runs in CI/CD. It catches regressions before they hit production.

A simple append-only log store for audit trails. Nothing fancy. PostgreSQL with a timestamp, trace ID, agent ID, action type, input hash, and output hash. PII gets stripped at write time.

The best AI implementations are boring. They just work. Klarna's AI assistant handles the workload of 700 full-time employees with 80% faster resolution times. That didn't happen because they picked the coolest framework. It happened because they built observability into every layer from day one.

If you can't replay last Tuesday's failed agent run step by step right now, your stack has a problem. And it's not a framework problem. It's an observability problem.

Frequently Asked Questions

What are evals in agentic AI?

Evals are automated tests that check whether an AI agent's output is correct, safe, and useful before it reaches a user or triggers an action. Tools like Promptfoo and Ragas let you define pass/fail criteria. Think of them like unit tests for your agent's judgment. Run them in CI/CD to catch regressions before production.

What should be logged in an AI agent audit log?

Every audit log entry needs a timestamp, trace ID, agent ID, the action taken, the input received, and the output produced. For PII safety, hash or redact sensitive fields at write time but keep the trace ID so you can reconstruct the full decision chain. StoryPros uses append-only PostgreSQL tables for this. Simple and queryable.

Could you realistically audit your AI agents today?

Most teams can't. If you're running agents without replayable traces and structured audit logs, you can't answer basic questions like "why did the agent send that email?" or "what data did it see when it made that decision?" Adding OpenTelemetry tracing and a basic log store takes about 2-4 weeks. It's the single highest-ROI investment you can make in your agent stack.

Is AI taking over audits?

AI is augmenting audits, not replacing auditors. Klarna uses AI to handle 2.5 million customer conversations, but every decision is traceable through LangSmith. The real shift is that AI agents now generate so many decisions per hour that manual auditing is impossible. You need automated eval gates and structured logs to keep up. The auditor's job is changing from reviewing individual decisions to reviewing the systems that make decisions.

How much does poor AI agent observability cost?

For a typical 10-agent workflow with a 2% tool-call failure rate, the difference between 30-minute and 5-minute mean time to resolution is about 41 hours per month. At $150/hour fully loaded engineering cost, that's $6,250 in wasted debugging time alone. That doesn't count the revenue lost from false actions, bad customer interactions, or the reputational damage when an agent goes off the rails in production.