BackMA
16+ years of experience building production-grade systems across LLM orchestration, agentic workflows, RAG pipelines, and cloud infrastructure. Would love to partner up with a GTM expert. Or anyone with over 7 years in sales or BD or marketing or all of them combined.
In the era of autonomous AI agents, observability is no longer just an operational concern. It has become a core part of the system itself.
If you're building agentic applications, a production-grade observability architecture should include:
⭕ An open observability stack
⭕ End-to-end data capture
⭕ Correlation IDs across every workflow
⭕ Evaluation of decisions, not just outputs
⭕ Monitoring beyond infrastructure metrics
⭕ Closed feedback loops that feed operational data back into agents
The last point is the most important.
Observability is no longer only about debugging. It's the feedback layer that enables agents to learn, adapt, and improve over time.
Before designing the agent, design the observability layer underneath it.
Prompts can improve what agents say and do. Observability is what enables continuous improvement at scale.
Detailed breakdown sistava.com/en/insights/observability-first-ai-…
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