Most teams waste weeks — sometimes months — debating agent frameworks.
Meanwhile, nothing meaningful reaches production.
This article exists to prevent that.
Not from theory. From real experience building production-grade multi-agent systems for startups from pre-seed to Series B.
👉 If you’d rather make this decision with us and ship it to production, book a 30-minute call.
The real mistake (before frameworks even matter)
Let’s be blunt:
The framework is almost never the real problem.
We’ve shipped serious production systems using:
- CrewAI
- Agno
- LangGraph
- and custom hybrids
We’ve also seen teams fail with all three.
The difference was never the framework. It was who owned the full system.
Frameworks matter less than you think
The hard problems in multi-agent systems are always the same:
- integration with real systems
- error handling and recovery
- output evaluation
- latency and cost control
- deployment and monitoring
- handoff to internal teams
All of that is framework-agnostic.
Choosing well can save you weeks. Choosing poorly rarely kills the project. Lack of execution does.
The framework we use — depending on the case
CrewAI — when workflows mirror human collaboration
We use CrewAI when:
- the system mirrors human roles working together
- responsibilities are clearly defined
- speed of initial execution matters
Real example: An AI marketing system built around defined agent roles. CrewAI fit naturally.
⚠️ Not ideal for:
- latency-critical systems
- fine-grained control
- high-performance workloads
Agno — when latency is non-negotiable
Agno is our choice when:
- throughput is high
- response time matters
- low-level control is required
Real example: TrustaNova — 15,000+ disputes per year, 78% resolved by AI alone, sub-second latency. Here, performance wasn’t optional.
LangGraph — when complexity dominates
LangGraph makes sense when you have:
- cycles and branching logic
- explicit state management
- humans in the loop
Real example: Document processing pipelines with validations, conditional routing, and human checkpoints.
More powerful. More complex. Not for everything.
How we actually make this decision with clients
The questions that matter are:
- What’s your latency budget?
- Does your workflow resemble a human team?
- Do you need cycles, branching, or state?
- How mature is your engineering team?
- Are you already invested in LangChain?
These questions matter more than benchmarks.
The uncomfortable truth
If you’ve spent more than a week debating frameworks, you’re optimizing the wrong thing.
Teams that reach production:
- choose something reasonable
- commit
- execute with clear ownership
That’s the difference between demos and real systems.
Where FACTA comes in
FACTA exists to close this gap: from “we know which framework to use” to “we have a system running in production”.
Startups work with us as:
- a fractional CAIO (real technical ownership)
- an execution partner (from decision to deploy)
- a technical partner for business-critical AI systems
Led by:
- Matías Baglieri — production AI systems, architecture, and execution
- Carolina Fogliato — operations, governance, and scaling systems inside real companies
We don’t give opinions. We stay until the system works.
Closing thought
The framework that matters is the one that actually ships to production.
If you want to keep reading, this blog will help.
If you want to reach production without paying the cost of mistakes:
👉 Book a 30-minute conversation. No pitch. No pressure.
