Learnings while working with long-running AI agents

#genai #agents #aws

One of my biggest learnings while working with long-running AI agents is that logging and progress reporting are not optional features when the agent is tightly coupled with a UI — they are part of the product experience itself.

Initially, I used to think of logging mainly from a debugging or engineering perspective. But with agentic systems, especially long-running workflows involving multiple tools, reasoning steps, APIs, retries, or multi-agent coordination, I realized users experience “silence” very differently than traditional applications.
When an agent takes 30 seconds, 2 minutes, or longer without visible progress, users immediately start questioning:

Is the system stuck?
Did my request fail?
Is it doing the wrong thing?
Should I refresh or retry?

That uncertainty destroys trust very quickly.
I learned that users do not just want the final answer — they want confidence that the system is actively working toward the answer. Progress visibility creates psychological assurance. Even simple updates like:

“Analyzing uploaded documents…”
“Fetching data from CRM…”
“Generating recommendations…”
“Validating final response…”

dramatically improve user confidence and patience.
Another major realization was that long-running agents are fundamentally non-deterministic systems. Unlike traditional APIs, agents can:

take different execution paths,
loop through reasoning,
invoke tools dynamically,
retry failed steps,
or spend time resolving ambiguity.

Without structured logging and traceability, debugging becomes extremely difficult because the same input may not always produce the same internal execution path. Modern AI observability emphasize tracing tool calls, reasoning paths, latency, token usage, and execution flow because agent behavior is inherently complex and probabilistic.

I also learned that progress reporting is not only for users — it becomes equally important for engineering and operational visibility. Once agents move into production, observability helps teams identify:

where workflows slow down,
which tool calls fail,
why latency spikes happen,
and where hallucinations or execution deviations originate.

One practical lesson I learned is that UI-integrated agents should expose execution state intentionally, not dump raw logs. There is a difference between:

engineering telemetry,
operational traces,
and user-friendly progress communication.

Users need understandable milestones, while engineers need deep execution traces.
Another important learning was around perceived performance. In many cases, improving progress visibility improved user satisfaction more than reducing actual latency. A 90-second process with clear step-by-step reporting often feels faster and more reliable than a silent 40-second execution.

Today, I strongly believe that for long-running AI agents: