The Distributed Systems Problem: Why AI Agents Break in Production
If you’ve shipped an AI agent to production, you know the Day 2 problem. Over 90% of AI agents fail before production. The models work. The infrastructure doesn’t. We’re running agents - long-running, stateful, autonomous workflows - on systems designed for stateless request-response.
When a 45-minute agent workflow dies at step 38 because your server restarted, you’ve lost more than an error log. You’ve burned tokens, API quota, and user trust. And unlike a failed HTTP request, you can’t just “retry” - the agent’s reasoning state is gone.
I’ve spent the last year building AI infrastructure. The tooling is shockingly immature. Every team hits the same failure modes and rebuilds the same broken solutions: brittle retry logic, state management hacks, rate limiting Band-Aids.
The problem isn’t the agents. It’s the infrastructure gap.
Agents Break Every Infrastructure Assumption
Assumption #1: Fast & Stateless
Traditional apps: Handle request → return response → forget everything. Average response time: 50ms.
Agents: Multi-step reasoning loops that run for minutes or hours. Call 20 different APIs. Make stochastic decisions at each step.
The break: A single network blip or worker restart loses the entire workflow state. You can’t “just replay” because the agent’s call stack, local variables, and reasoning context are gone. No amount of retries will help you.
Assumption #2: Concurrency is Request-Scoped
Traditional apps: Each request is isolated. One user’s failed request doesn’t affect anyone else.
Agents: Multiple agents running in parallel, all sharing the same API quotas, all racing to read and modify shared state.
The break:
The rate limit cascade: Agent A is debugging a loop and burns through your OpenAI quota in 30 seconds. Agents B through Z all start failing with 429s. Your entire system is down because one agent misbehaved.
The state race: Agent 1 reads user context at 10:00:00. Agent 2 modifies it at 10:00:01. Agent 1 writes based on stale data at 10:00:02. The user’s context is now corrupted, and you have no idea which agent’s view was “correct.”
The resource starvation: One runaway agent spawns 50 parallel reasoning branches and starves everything else of memory and compute.
You need system-wide rate limiting (not per-agent), distributed locks, and transactional state management. None of this exists in standard application frameworks. You’re on your own.
Assumption #3: Deterministic Execution
Traditional software: Same input → same output. Bugs are reproducible. You read the stack trace, fix the code, deploy.
Agents: Stochastic decision-making at every step. The same prompt can produce different tool calls, different reasoning paths, different outcomes.
The break: Your agent hallucinates a tool call. Or it enters an infinite reasoning loop. Or it chooses Tool B when Tool A was obviously correct.
Your logs tell you what happened (”Agent called send_email with invalid parameters”). They don’t tell you why (was the context corrupted? did the model misinterpret the schema? did a previous tool call return bad data?).
Traditional monitoring - CPU graphs, error rates, p99 latency - is useless here. You need decision-level observability: why did the agent make that choice?
Assumption #4: User-Scoped Auth
Traditional apps: User clicks button → auth token attached to request → action performed → token discarded.
Agents: Act on your behalf while you’re offline. Call 15 different APIs autonomously. Make decisions that require different permission levels depending on context.
The break: Traditional OAuth wasn’t built for this. It assumes a human is present to click “Authorize” and handle browser redirects. Agents are headless - they act while the user is offline, asleep, or unreachable.
You can’t give the agent your master API key - that’s a security disaster. You can’t pre-scope every possible action - you don’t know what the agent will need until runtime.
You need dynamic, just-in-time credential delegation. Short-lived tokens scoped to exactly what the agent needs right now. And when the agent wants to do something sensitive - delete a database, charge a credit card - you need human-in-the-loop approval gates that pause execution, wait for a signature (sometimes for hours), and resume without losing state.
Your auth infrastructure doesn’t do this. You’re going to build it yourself, and you’re going to get it wrong the first three times.
Assumption #5: Services Talk Via Contracts
Traditional microservices: Rigid REST or gRPC interfaces. Typed schemas. You call GET /api/v2/items?sort=price&limit=10 and you get exactly what you asked for.
Agents: Communicate in high-level intents. “Find me the best option.” “Summarize the user’s preferences.” “Coordinate with the scheduling agent to find a time.”
The break: Agent-to-agent communication isn’t just JSON over HTTP. It requires:
Context propagation: Agent B needs to know what Agent A was thinking, not just what data it returned.
Shared working memory: A persistent space where agents can read and write state across different servers and lifetimes.
Reasoning handoff: Agent A partially solves a problem, hands off to Agent B with full context, Agent B picks up where A left off.
None of this exists in your service mesh. You’re going to build a custom “agent communication layer” and spend six months debugging context drift.
What Reliable Agents Actually Require
If you want agents to work in production - not in a demo, not in a Jupyter notebook, but under real load with real users - you need infrastructure that doesn’t exist yet.
1. Durable Execution
Not checkpoints. Not retries. Guaranteed resumption.
If a worker node dies mid-workflow, the agent must resume on another node with its call stack, local variables, and reasoning state intact. Progress isn’t “saved” - it’s guaranteed. The agent picks up at the exact line of code where it stopped, as if nothing happened.
This is how Temporal works for workflows. Agents need the same semantics. However, unlike a standard Temporal workflow, an agent’s path isn’t hardcoded in a DSL - it’s generated live by an LLM. This makes the durability requirement even more extreme: you aren’t just persisting data; you’re persisting a dynamic, evolving reasoning chain.
2. Global Concurrency Control
System-wide rate limiting: All agents share a single budget for OpenAI calls. If Agent A is burning tokens, Agents B-Z slow down proportionally. No cascading failures.
Distributed coordination: Agents competing for the same resource (user state, external API, database row) use distributed locks or optimistic concurrency control. State corruption is impossible.
Resource quotas: Runaway agents are killed before they starve the system. No single agent can take down production.
3. Decision-Level Observability
Traditional logs: “Agent called Tool B at 10:00:03.”
What you need: “Agent chose Tool B over Tool A because the user’s context indicated preference X, and Tool A’s output schema didn’t match the downstream agent’s expectations.”
You need to trace the reasoning, not just the execution. Every decision point, every branch, every piece of context that influenced the outcome.
And when an agent fails, you need to replay its thought process, not just its API calls.
4. Delegated Identity & Dynamic Scoping
Just-in-time credentials: When an agent needs to call Stripe, the infrastructure issues a short-lived token scoped to exactly the Stripe API and the specific user context. The token expires in 60 seconds. The agent never sees your master key.
Approval gates: When the agent wants to execute a sensitive action, execution pauses. A notification goes to the user. The user approves or rejects (sometimes after several hours). Execution resumes with full state intact. The agent doesn’t restart from scratch.
Audit trails: Every action the agent takes is logged with full attribution. Who authorized it? What context led to the decision? What credentials were used?
5. Agent Communication Primitives
Shared memory: A durable, transactional store where agents can read and write state. Agents running on different servers, at different times, can access the same working memory.
Context propagation: When Agent A hands off to Agent B, B receives not just data but the history of reasoning that produced that data. B doesn’t start from zero.
Handoff semantics: Agent A pauses. Agent B takes over. Agent A resumes later. The infrastructure manages the transition without dropping state.
The Infrastructure Maturity Gap
Here’s what most teams do today:
Build agents with Python scripts, cron jobs, and manual restarts
Add retry logic in application code (and get it wrong)
Store state in Redis or Postgres with custom serialization (and lose data during crashes)
Rate-limit by hoping agents don’t call OpenAI too fast
Debug failures by reading logs and guessing what the agent was thinking
Every team rebuilds the same broken infrastructure. The solutions are fragile, incomplete, and impossible to test.
Your team spends more time on infrastructure duct tape than on the agent itself. The work that actually differentiates your product - better reasoning, smoother user experience - barely gets attention.
The Shift We Need
From: Application-layer duct tape
To: Infrastructure that handles state, concurrency, auth, and observability so you don’t have to
Agents are powerful. But right now, they’re production nightmares.
The teams that win won’t be the ones with the best prompts or the biggest models. They’ll be the ones who solved the infrastructure problem.
It’s time to stop building agents like scripts and start building them like distributed systems.
Because that’s what they are.