🏛️ Layered Architecture of Agentic AI Across Product Releases

December 9, 2025

3 min read

One of the most practical questions every builder faces is: What are the minimal requirements for Agentic AI–specific tooling and infrastructure that you can "get away with" as your product moves through its lifecycle?

Instead of over-engineering from day one, the goal is to progressively adopt only what you truly need at each stage:

POC → MVP → Beta → GA → Full-Scale Agentic Systems

Enterprise AI Product Lifecycle — End-to-End Execution Flow

This visual representation may become outdated over time, as the Agentic AI ecosystem is evolving rapidly and new tools, platforms, and frameworks are continuously emerging.

Below is a clear, incremental breakdown of what each phase minimally requires.

🧪 1. POC (Proof of Concept)

At the POC stage, the objective is speed of validation, not perfection.

Foundation Models layer
Base Infrastructure layer
GPU/CPU Hardware layer

The idea works
The model can solve the core problem
The system is technically feasible

No heavy production tooling is required yet — just enough to demonstrate value.

🛠️ 2. MVP (Minimum Viable Product)

Once the concept is validated, the next step is to make it usable for real users.

Everything from POC
Data Storage layer
Orchestration layer

Persist internal and external context
Chain multiple model calls
Produce repeatable user-level workflows

This is where your Agentic System begins to look like a real product.

🧍‍♂️ 3. Beta

At the Beta stage, real usage exposes stability and scale limitations.

Everything from MVP
Model Routing layer
LLM Observability

Route between multiple model providers
Handle partial failures
Trace every step taken by agents
Monitor prompts, responses, latency, and errors

This is where the system transitions from functional to operationally observable.

🏗️ 4. GA (General Availability)

Once you go public, correctness, safety, and trust become non-negotiable.

Everything from Beta
LLM Evaluations
LLM Security

Continuously validate output quality with evaluation pipelines
Protect internal systems and user data with security guardrails
Detect regressions and risks before they reach end users

This is the phase where your Agentic System must be reliable at enterprise scale.

🤖 5. Full-Scale Agentic Systems

This is the stage where your system becomes a true autonomous, networked intelligence layer.

Everything from GA
Agent Memory
Communication Protocols

Retain long-term and short-term memory
Share context across distributed systems
Communicate using standardised protocols
Operate as part of the Internet of Agents (IoA)

This is where single AI applications evolve into distributed Agentic ecosystems.

✅ In Summary

POC → Models + Infra + Compute
MVP → Add Data + Orchestration
Beta → Add Routing + Observability
GA → Add Evaluation + Security
Agentic Systems → Add Memory + Communication Protocols

Move fast in early stages
Avoid premature infrastructure complexity
Scale safely and intelligently as the system matures

🏁 Final Summary

Agentic Systems might look simple from the outside, but it takes a lot of effort to bring them to production reliably.

The proliferation of vendors in the AI space, due to the heat of the market, is an amazing benefit we get as builders.

You should carefully think about the minimal amount of additional infrastructure elements you adopt as your AI application moves along the maturity curve.

Finally, adopting Evaluation-Driven Development is key — especially when building for enterprises.