Deploying AI agents in production is trickier than most teams expect. What works perfectly in development often becomes a reliability nightmare once real traffic hits.

After looking at incident reports, some clear patterns emerge. The same few issues keep causing the majority of production failures.

42% of AI agent failures come from hallucinated API calls, and another 23% are GPU memory leaks. These aren't edge cases - they're systematic problems that need systematic solutions.

Here's what's actually breaking and how to prevent it.

Common failure patterns

Hallucinated API calls

LLMs generate code that looks correct but calls non-existent methods or deprecated endpoints. Traditional validation tools miss this because the code is syntactically valid - it just references APIs that don't exist in your environment.

Teams often spend significant time debugging what appears to be infrastructure issues when the root cause is the AI making incorrect assumptions about available APIs.

GPU memory leaks

A known vulnerability in AMD, Apple, and Qualcomm GPUs can cause AI workloads to leak over 180MB per inference cycle. In Kubernetes environments, this can cascade across pods and eventually crash entire nodes.

Standard monitoring often doesn't catch this until resource exhaustion is already occurring.

Cascading failures

AI agents are more interconnected than typical microservices. A single malformed operation can stall agent threads for extended periods, and recovery processes often reset accumulated context, leading to broader system failures.

Insufficient observability

Most teams monitor traditional infrastructure metrics but lack visibility into AI-specific behavior like GPU utilization patterns, token consumption, and model performance degradation.

Practical solutions

Constrain API generation

Instead of relying on post-generation validation, limit what the LLM can suggest in the first place by providing explicit API context:

# Extract what's actually available
global_deps = extract_imports(codebase)
local_deps = parse_function_calls(current_module)

# Tell the LLM what it can actually use
prompt = f"""
Available APIs: {global_deps}
Local functions: {local_deps}
Task: {user_request}
"""

Teams using dependency-constrained prompting report fewer API hallucinations. The approach is straightforward: if you don't tell the LLM about APIs that don't exist, it's less likely to invent them.

Implement GPU resource controls

Set explicit resource limits in your container orchestration:

resources:
  limits:
    nvidia.com/gpu: 1
    memory: "4Gi"
  requests:
    memory: "4Gi"
    cpu: "2"

Monitor GPU memory usage and restart containers before they crash:

#!/bin/bash
while true; do
  vram_usage=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits)
  if [ $vram_usage -gt 7500 ]; then  # 90% of 8GB
    kubectl rollout restart deployment/ai-agent
  fi
  sleep 30
done

This type of proactive monitoring has reduced OOM crashes in production environments.

Version AI components as units

AI agents consist of multiple interdependent components: models, vector databases, prompt templates, and configuration. These should be versioned and deployed together:

# ai-agent-chart/Chart.yaml
dependencies:
  - name: llm-model
    version: "1.2.3"
  - name: vector-db
    version: "0.9.1"
  - name: prompt-templates
    version: "2.1.0"

Deploying the entire bundle as a unit prevents version mismatches that can cause subtle but significant failures.

Add AI-specific monitoring

Traditional APM tools don't capture AI-specific metrics. You need to track GPU utilization, token consumption, and model performance alongside business outcomes. OpenTelemetry provides a good foundation for this:

from opentelemetry import trace
import time

tracer = trace.get_tracer(__name__)

def ai_inference(prompt, user_id):
    with tracer.start_as_current_span("ai_inference") as span:
        start_time = time.time()

        span.set_attribute("prompt.length", len(prompt))
        span.set_attribute("user.id", user_id)

        response = model.generate(prompt)

        span.set_attribute("response.length", len(response))
        span.set_attribute("inference.duration", time.time() - start_time)
        span.set_attribute("tokens.consumed", count_tokens(prompt + response))

        return response

Correlating these metrics with infrastructure data helps identify when GPU pressure affects response quality.

Build resilient fallback systems

Implement circuit breakers for external API calls:

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=2, max=10)
)
def call_external_api(endpoint, payload):
    response = requests.post(endpoint, json=payload, timeout=10)
    response.raise_for_status()
    return response.json()

Have a clear escalation path when AI components fail:

def ai_with_fallback(user_request):
    try:
        return ai_agent.process(user_request)
    except AIAgentError:
        return rule_based_handler.process(user_request)
    except Exception:
        escalate_to_human(user_request)
        return "Request escalated to support team"

Making AI agents production-ready

AI agents in production require the same operational discipline as any other critical system. The difference is that they have unique failure modes that traditional monitoring and deployment practices don't address.

Teams that succeed treat AI agents as complex distributed systems with proper observability, resource management, and graceful degradation. The ones that struggle try to deploy them like traditional applications.

The good news is that once you address these systematic issues, AI agents become much more predictable and reliable in production environments.