Industrial Robotics
Autonomous systems break in the field, not just in logs. This post explores why real-world observability depends on structured replay, not just dashboards, and how to design for clarity when the cloud can’t help.
It Always Starts the Same Way
Something breaks: a robot stalls, a camera freezes, a line stops. Everyone asks, "What happened?"
Cloud systems have answers: logs, metrics, traces. It's all there, queryable, timestamped, correlated. But field operations are different. On-prem, in factories, behind firewalls, debugging becomes detective work: piecing together VPN-pulled logs, grainy video, stale metrics, and engineers' best guesses. The cloud-native assumption that telemetry streams continuously and environments are uniform simply doesn't hold.
Observability, in the Real World
Our clients deploy critical systems in high-stakes, often isolated environments: robots in factories, automation in warehouses, devices in secure facilities. Cloud tooling fails here.
We bridge this gap by deploying lightweight edge agents. Instead of streaming firehose telemetry, these agents buffer, bundle, and replay structured session data—logs, video, inputs, sensor traces, system states—right where the action happens. They don't replace your observability stack; they complete it.
This Pattern Has Precedent
Every industry that builds complex systems in the physical world eventually converges on the same foundational approach:
Capture. Align. Replay. Understand.
It shows up everywhere.
Telecom, 1990s
When calls dropped, telcos couldn't rely on logs alone. They built session trace tooling spanning towers to firmware—capturing the full call path so field teams could reconstruct exactly what happened, in sequence, from first signal to failure.
Apollo 12, 1969
A lightning strike wiped out all telemetry during launch. Flight controllers had no logs, no metrics, no visibility into system state. The only reason the mission continued was one engineer—John Aaron—who recognized the failure pattern from a prior simulation and called for a single command: "SCE to AUX."
It worked. But it was a close call, and it relied entirely on human memory.
That's the reality in the field today. Systems fail. Connectivity drops. Logs vanish. When they do, you need more than live metrics—you need a replayable record of what actually happened. Not so someone has to remember it. So no one has to.
Our Belief: Real Observability Requires Replay
We don't build dashboards. We build infrastructure that lets teams reconstruct reality, frame by frame, log by log, input by input. In the field, debugging is about understanding what happened narratively and causally.
Capture Sessions, Not Just Logs. We capture time-aligned session archives: logs, telemetry, operator input, sensor data, and video.
Build Replay Interfaces for Humans. We create human-debuggable, searchable UIs tailored to your domain—like custom Foxglove Studio panels for robot debugging—so operators and PMs can triage without needing to pull in senior engineering.
Tie Everything to Code + CI. Every replay links to the deployed code, CI artifacts, and version control. You know exactly what was running when things broke.
Run Agents Where the Cloud Can't Go. Our field-native agents buffer intelligently and upload when connected—even in air-gapped or intermittently connected environments.
Make Debugging a Team Sport. When replay is structured and accessible, issues don't require a senior engineer to reconstruct from memory. Operators, PMs, and support teams can triage collaboratively.
This Is What We Build at Andes Path
At Andes Path, we build tools for usable, debuggable, and reliable autonomous systems. Our field agents, replay systems, and capture pipelines transform fragmented telemetry into clear, actionable records, so teams can understand what their systems actually did, not just what a dashboard suggests.
We're a growing engineering team from Santiago, embedded with ambitious companies building real physical systems. We work on the hard parts: the factory floor, the secure facility, the robot that has to keep working when there's no cloud in sight.
If your team has ever spent more than a day debugging a field incident from VPN-pulled logs and engineers' memory, we should talk. Real observability is about precise replay, clarity, and control, not AI-generated summaries or prettier dashboards.
