Lessons Learned Running an Autonomous Agent Overnight: The Four Failure Modes That Actually Matter

I left an autonomous agent stack running overnight to scrape, summarize, and post curated content to a staging site. By morning, the agent had posted 47 articles — but 12 were duplicates, 3 contained hallucinated facts, and 2 were stuck in infinite loops consuming 8GB of RAM each. The stack didn't crash, but it was producing garbage at scale. Here's what I learned.

The Setup: What We Ran

The agent was a simple three-stage pipeline:

• Scraper: A Python script using httpx to fetch RSS feeds and web pages.
• Summarizer: A fine-tuned Mistral 7B via vLLM, running on a single A10G, with a 4k context window.
• Poster: A Node.js service that pushed formatted articles to a WordPress REST API.

Orchestration was done via a bash loop with a 30-second sleep between iterations. No checkpointing, no state persistence, no circuit breakers. This was a mistake.

Failure Mode #1: Context Poisoning (The Silent Killer)

The agent's summarizer accumulated context across runs because we reused the same vLLM instance without resetting the chat history. By iteration 15, the context window was filled with previous summaries, causing the model to produce outputs that were increasingly influenced by earlier tasks. One summary started with "As we discussed earlier..." — a clear sign of context leakage.

Fix: Implement explicit context isolation per iteration. We switched to a fresh vLLM instance per task using a Docker container lifecycle, which added ~200ms overhead per task but eliminated poisoning entirely.

# Before (poisoned)
from vllm import LLM
llm = LLM(model="mistral-7b-instruct")
for url in urls:
    result = llm.generate(prompt)

# After (isolated)
import subprocess
def summarize(text):
    result = subprocess.run([
        "docker", "run", "--rm",
        "vllm:latest",
        text
    ], capture_output=True, text=True)
    return result.stdout

Failure Mode #2: Loop Detection (The Infinite Spiral)

Two agents entered infinite loops: one kept re-scraping the same page because the content hash matched a previous article, and another kept retrying a failing API call without exponential backoff. The loops consumed CPU and RAM until the OOM killer stepped in.

Fix: Implement idempotency keys and a sliding window dedup cache. We used a Redis set with TTL to track processed URLs and content hashes. For retries, we used tenacity with exponential backoff and a max retry count of 3.

import redis, hashlib, tenacity

r = redis.Redis()

def process_url(url):
    content_hash = hashlib.sha256(url.encode()).hexdigest()
    if r.sismember("processed", content_hash):
        return  # skip duplicate
    
    @tenacity.retry(stop=tenacity.stop_after_attempt(3),
                    wait=tenacity.wait_exponential(multiplier=1, min=4, max=10))
    def fetch():
        resp = httpx.get(url, timeout=10)
        resp.raise_for_status()
        return resp.text
    
    text = fetch()
    r.sadd("processed", content_hash)
    r.expire("processed", 86400)

Failure Mode #3: Resource Leak Cascades (The Silent Drain)

Each agent iteration opened a new HTTP connection, loaded a model, and spawned subprocesses. Over 14 hours, that's ~1680 iterations. File descriptors leaked, GPU memory fragmented, and Python's garbage collector couldn't keep up. By iteration 800, the system was swapping.

Fix: Implement strict resource limits per iteration using context managers and explicit cleanup. We also added a watchdog that kills any process exceeding 2GB RSS.

import resource, signal, os

def enforce_memory_limit(max_mb=2048):
    def handler(signum, frame):
        raise MemoryError("Exceeded memory limit")
    signal.signal(signal.SIGXCPU, handler)
    resource.setrlimit(resource.RLIMIT_AS, (max_mb * 1024 * 1024, -1))

with httpx.Client() as client:
    enforce_memory_limit()
    # do work

Failure Mode #4: Silent Model Drift (The Subtle Rot)

The Mistral model was not static; it was being served by vLLM with a dynamic batching queue. Over time, the model's output distribution shifted due to KV cache fragmentation and floating-point accumulation errors. We saw token probabilities diverge by 0.3% after 500 iterations — enough to change a summary's sentiment.

Fix: Periodically reload the model weights every 100 iterations. We also switched to a stateless inference setup where each request was a fresh inference without KV cache reuse.

import time

RELOAD_INTERVAL = 100
iteration = 0

while True:
    if iteration % RELOAD_INTERVAL == 0:
        # Reload model
        llm = LLM(model="mistral-7b-instruct", kv_cache_dtype="fp8_e4m3")
    
    result = llm.generate(prompt)
    iteration += 1
    time.sleep(30)

The Failure Modes That Do Not Matter

• Token limit errors: They happen, but they're easy to catch and retry with truncation.
• Network timeouts: Retry with backoff handles these trivially.
• Model hallucinations: Important, but they're a quality issue, not a reliability one. Our focus was uptime, not accuracy.
• Rate limiting: Standard HTTP 429 handling works fine.

System Architecture After Fixes

We redesigned the stack into a stateful, resilient pipeline:

1. Scheduler: A cron job that enqueues tasks to Redis (using RQ).
2. Worker Pool: 4 workers, each with its own Docker container for isolation.
3. Checkpointer: Every successful iteration writes a checkpoint to PostgreSQL.
4. Dead Letter Queue: Failed tasks go to a separate Redis list for manual review.

# docker-compose.yml
services:
  worker:
    image: agent-worker:latest
    environment:
      - REDIS_URL=redis://redis:6379
      - DATABASE_URL=postgres://postgres:password@db:5432/agents
    deploy:
      replicas: 4
      resources:
        limits:
          memory: 4G
  redis:
    image: redis:7-alpine
  db:
    image: postgres:15-alpine

Key Metrics After Fixes

• Uptime: 100% over 72 hours (no crashes).
• Duplicate posts: 0 (down from 12).
• Loop incidents: 0.
• Memory usage per worker: Stable at 1.2GB ± 100MB.
• Average iteration time: 45 seconds (up from 30 due to Docker overhead, but worth it).

Lessons for Your Own Agent

1. Isolate everything: Each task should be a fresh process or container. The overhead is negligible compared to debugging corruption.
2. Instrument everything: Log memory, latency, and output hashes. We missed the drift because we weren't tracking output distributions.
3. Assume every long-running agent will fail: Design for idempotency and graceful degradation. Our Redis-based dedup was a lifesaver.
4. Test with overnight runs: Unit tests won't catch cascading failures. Let it run for 8 hours before trusting it.

Running autonomous agents overnight is a stress test of your infrastructure as much as your model. The four failure modes above are not exotic — they're the daily reality of production agentic systems. Fix them before they fix you.