Building observability tools for AI agents
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Hi Product Hunt,
I’m Sharad from India. I spend most of my time building AI products and experimenting with agent workflows, observability, and developer tooling.
Recently I’ve been focused on how AI agents behave in production — debugging failures, tracking costs, understanding handoffs between agents, and making these systems easier to operate reliably.
Currently building AgentPulse, an observability platform for AI agents and multi-agent workflows.
Excited to connect with other builders here and discover what people are shipping.
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Hello Sharad, welcome.
Observability is a must, but overhead is a killer. Is AgentPulse sandboxed?
How do you ensure it doesn't cause latency spikes or impact the reliability of agents in production?
Hey @lyshen
You’re absolutely right — overhead was one of the first things we optimized for.
AgentPulse runs asynchronously in the background. Once your agent completes a step, telemetry is queued immediately and sent separately, so your application flow isn’t blocked waiting on observability calls.
Think of it like dropping a letter in a mailbox and continuing your day — delivery happens independently.
And importantly: if AgentPulse experiences downtime, your agents continue running normally. At worst, you temporarily lose observability data — not application reliability.
@sharad_gumber That makes total sense for protecting application reliability!
However, with LLM agents, one of the biggest production fears is runaway loops where an agent gets stuck and burns through API credits in minutes. Since your telemetry runs asynchronously, it acts more like a post-event audit.
How does AgentPulse handle real-time kill-switches or budget alerts before the wallet bleeds dry?
@lyshen AgentPulse alerts you the moment a run lands — but a true kill-switch is on the roadmap: a pre-flight budget check in the SDK that stops the agent before the next call, not after.
Very into this category. One thing we've learned building around agent control is that observability only becomes useful once it can answer a pretty simple question: should the run keep going?
The expensive failures usually aren't spectacular. It's just the same uncertainty repeated politely until someone notices the bill.
We're building MartinLoop around that side of the problem, so it's always nice to see more people tackling the visibility layer too.
Very into this category. One thing we've learned building around agent control is that observability only becomes useful once it can answer a pretty simple question: should the run keep going?
The expensive failures usually aren't spectacular. It's just the same uncertainty repeated politely until someone notices the bill.
We're building MartinLoop around that side of the problem, so it's always nice to see more people tackling the visibility layer too.
This is a smart place to build. The teams that become trusted fastest usually separate observability from storytelling: what changed, what was verified, and why the next retry was justified. That discipline matters more than flashy agent demos once the system touches real workflows.