I spend most of my day designing, debugging, or defending systems — enterprise-scale software integrations, GenAI pipelines, and multi-team workflows that stretch from here to tomorrow. It’s rewarding work, but let’s be honest: it’s a little abstract. Systems thinking is about patterns, interdependencies, feedback loops, and emergent behaviors. Which is great — until you forget that you’re part of a system too, and that system includes a coffee maker that decides to malfunction during sprint planning.
Fortunately, I live with two very grounded systems thinkers: Smudge, a philosophical supermutt with the patience of a senior developer, and Sulley, a no-nonsense German Shepherd-Husky mix who could run our Incident Response team if I gave him admin access. While I’ve been building AI-enhanced tools and wrestling with stakeholder alignment, they’ve been quietly demonstrating systems mastery that would make any product owner jealous.
Here’s what they’ve taught me about real systems thinking:
1. Everything Is Connected — Even the Things You Can’t See
I used to treat “the backyard,” “my work laptop,” and “the neighbor’s trash day” as separate contexts. Clean boundaries. Discrete modules. Very engineer-like.
Smudge does not.
If trash is delayed by a day, Smudge knows. If I’m ten minutes late to my morning oats routine, Sulley shifts his weight just enough to make me feel it. If my Slack notifications are pinging more than usual, both dogs position themselves strategically between me and the kitchen — because experience has taught them that stressed humans forget treat schedules.
Dogs don’t compartmentalize. They see the whole system — sounds, routines, smells, moods, delivery trucks, and my typing intensity — as one interconnected mesh of signals. It’s like they’re running continuous integration tests on reality.
Lesson: When you’re architecting systems, don’t isolate modules just because your org chart does. Systems don’t care about your swimlanes, and neither do the problems that will inevitably cascade across them.
2. Feedback Loops Are Real-Time, Relational, and Often Nonverbal
When Sulley wants a walk, he doesn’t log a Jira ticket or send a Slack message. He stares. Intensely. Then nudges my elbow with the precision of a well-tuned API endpoint. Then sits on my foot like an immovable block of intent.
This is a feedback loop: gentle escalation based on unmet expectations, with clear success criteria (human movement toward door) and built-in retry logic.
If I ignore it too long, the loop accelerates: pacing, paw taps, theatrical sighs, and finally full-body flops of protest that would make any agile coach proud. The system adapts its approach based on my response patterns, much like a well-designed monitoring system should escalate alerts when thresholds are consistently ignored.
Lesson: Good systems react. Better systems adapt. The best systems anticipate. Build feedback loops into your process — not just in telemetry and dashboards, but in culture and communication patterns.
3. Redundancy Is Not Inefficiency — It’s Resilience
Sulley doesn’t trust a single point of failure. This dog has clearly internalized the lessons of Site Reliability Engineering without ever reading a Google paper.
If the front door is locked, he’ll check the side door. Then the garage. Then the window he saw open once during a storm in 2021. He maintains a mental catalog of all possible egress points, their historical availability patterns, and their likelihood of success based on time of day and human mood.
Smudge takes a different approach to redundancy. She has three backup plans for getting a treat, including one that involves looking soulfully betrayed while positioned strategically near the treat jar, and another that leverages Sulley’s more direct approach as a distraction technique.
Lesson: Redundancy feels wasteful until the system fails. Then it feels like foresight. Design for graceful degradation, not just happy path scenarios.
4. Emergent Behavior Isn’t a Bug — It’s the System Talking to You
One morning, Smudge started doing a weird hop-turn-sniff routine on our walk. At first I thought she was being eccentric — maybe the canine equivalent of a feature flag gone rogue. Then I realized: she was triangulating the precise location of a cat hiding under a car three houses down.
No one taught her that algorithm. It wasn’t in a playbook or user story. It emerged from instinct, experience, and the complex system of signals she’d learned to decode over thousands of walks. She’d developed her own pattern recognition system and was applying it in real-time to solve a novel problem.
Lesson: When your system does something unexpected, don’t immediately classify it as technical debt. Ask: is this noise, or is the system telling me something new about the environment it’s operating in?
5. Trust Is the Most Scalable Protocol
I don’t use leashes inside the house. I don’t need to.
We have an understanding built over time: I trust them not to bolt when the door opens. They trust me not to forget dinner or walk schedules. We’ve built that trust through thousands of tiny interactions, each one a small contract successfully executed. It’s like a distributed system that’s achieved consensus through repeated proof of reliability rather than Byzantine fault tolerance.
When systems trust each other — when your Dev, Ops, Compliance, and Product teams aren’t second-guessing every handoff — you get flow. You get velocity. You get the kind of calm that lets you focus on solving actual problems instead of defending against imaginary ones.
Lesson: Systems that distrust their components are brittle. Systems that over-control them are slow. Trust, once established, is faster than authentication, more flexible than authorization, and more effective than any API rate limiting.
6. Simplicity Is a Discipline, Not a Default
Smudge and Sulley don’t overengineer solutions. They optimize ruthlessly for what actually matters.
They know the shortest path to the park (accounting for traffic patterns and maximum sniffing opportunities), the least defended spot on the couch (corner position, optimal for human proximity and escape routes), and the precise frequency of bark needed to summon human attention without triggering “that’s enough” protocols.
They don’t track vanity metrics or maintain complexity for complexity’s sake. They track what works, iterate quickly, and deprecate behaviors that don’t deliver value. It’s lean methodology without the meetings.
Lesson: If your system needs a 20-slide onboarding deck to explain its basic functions, it’s probably not as elegant as you think. Nature optimizes for energy efficiency. So do dogs. Maybe we should too.
7. Context Switching Has Costs
Here’s something I learned during a particularly intense sprint: Sulley doesn’t multitask. When he’s focused on something — tracking a squirrel, analyzing a new smell, determining if that delivery truck represents a threat — he commits fully. No divided attention, no background processing of other priorities.
Smudge has taught me that sometimes the most productive thing you can do is lie in a sunny spot and process information passively. Not every moment needs to be optimized for output. Sometimes the system needs to consolidate, integrate, and prepare for the next burst of activity.
Lesson: Constant context switching isn’t productivity — it’s thrashing. Build systems that respect the cognitive and computational costs of task switching, both for humans and processes.
Final Thought: You Are Always in the System
I used to think systems thinking was about zooming out — seeing the big picture, abstracting away the noise, building models that captured the essential patterns while ignoring the messy details.
Now I realize: it’s also about zooming in. Sitting still. Watching how the parts feel when they interact. Understanding that systems are not just technical — they’re emotional, relational, environmental. And sometimes, delightfully furry.
Every walk I take with Smudge and Sulley is a small audit of the world around us. They remind me to listen harder to the signals I’m receiving, simplify ruthlessly toward what actually matters, and trust the feedback loops I’ve almost learned to ignore.
They don’t write code or attend retrospectives. But they absolutely debug my perspective on what makes systems work — and what makes them worth working on.
Now if you’ll excuse me, Sulley is giving me that look that means the system is detecting an unmet requirement for outdoor time. Some feedback loops cannot be ignored.