One critical factor to keep in mind for teams building with agents: Instead of focusing on what LLM to use, focus on context, guardrails &amp; observability layers.
Every serious agentic system eventually faces the same architectural fork: do you chain your agents, or do you fan them out?
The answer isn’t either-or, sometimes it’s both, and knowing when to use which is the new core skill of AI engineering.
The sequential workflow goes like this: Planner → Coder → Reviewer → Deployer.
Each agent inherits the previous one’s output, refines it, and passes it forward. It’s slower, but every handoff is a checkpoint. This is the shape of production engineering.
The parallel workflow flips the economics. One Planner decomposes the work, three Coders attack independent slices simultaneously, and an Integrator merges the result. You trade coordination overhead for wall-clock speed.
This is the shape of exploration, prototyping, and anything embarrassingly parallel.
But the agents are only half the story. What makes this an engineering system rather than a demo is everything around them: a Context Layer feeding shared state, Guardrails intercepting every output before it touches production, Observability capturing what actually happened, and an Adaptation &amp; Learning loop that closes the cycle by feeding outcomes back into the LLM Reasoning Layer.
The lesson for builders: don’t pick LLMs or agents first. Pick your topology first, then context layer and your safety surface, then your memory model (which usually comes under context). The LLMs &amp; agents are the easy part.

Pavan Belagatti

When building agentic AI systems, the critical architectural decision is choosing between sequential (chained) and parallel (fan-out) agent topologies — and knowing when to use each. Sequential workflows provide checkpoints at every handoff, while parallel workflows trade coordination overhead for speed. Beyond the agents themselves, production-grade systems require a context layer for shared state, guardrails to intercept outputs before they reach production, observability to capture runtime behavior, and a feedback loop for continuous improvement. The key takeaway: choose your topology and safety surface before selecting LLMs or individual agents.

Building with AI Agents? Keep this in mind