AI Agent Production Failures: What Breaks and How to Build Around It

Building an AI agent that actually works in production is harder than the demos make it look. Here's what breaks, why it breaks, and how to build around it.

The Demo vs Reality Gap

Demo agents: work on a clean task with predictable inputs, run once, produce impressive output.

Production agents: handle messy real-world inputs, run thousands of times, fail in ways the demo never encountered.

The gap is large. Here's what closes it.

Problem 1: Context Accumulation

Agents maintain conversation history to track what they've done. In long runs, the history grows until it:

• Hits the context window limit (200k tokens for Claude Sonnet)
• Slows down due to processing all that context on every step
• Starts hallucinating because earlier context gets "compressed" by the model

Fix: Periodic summarization + sliding window.

async def get_working_memory(history: list[Message], max_recent: int = 10) -> list[Message]:
    if len(history) <= max_recent:
        return history

    # Summarize everything except the most recent messages
    to_summarize = history[:-max_recent]
    recent = history[-max_recent:]

    summary_response = await claude.messages.create(
        model="claude-haiku-4-5",  # Cheap model for summarization
        max_tokens=512,
        messages=[{
            "role": "user",
            "content": f"Summarize the key facts and decisions from this agent session in 3-5 bullet points:

" +
                       "
".join(f"{m.role}: {m.content[:200]}" for m in to_summarize)
        }]
    )

    summary_text = summary_response.content[0].text
    return [
        Message(role="system", content=f"Session summary: {summary_text}"),
        *recent
    ]

Problem 2: Tool Failure Cascades

When a tool fails, the naive agent either stops or loops. Neither is acceptable in production.

Fix: Structured error handling with recovery strategies.

async def call_tool_with_recovery(
    tool_name: str,
    args: dict,
    max_retries: int = 2
) -> str:
    last_error = None

    for attempt in range(max_retries + 1):
        try:
            result = await execute_tool(tool_name, args)
            return result
        except ToolTimeoutError as e:
            last_error = f"Timeout after {e.seconds}s"
            if attempt < max_retries:
                await asyncio.sleep(2 ** attempt)  # Exponential backoff
        except ToolRateLimitError as e:
            last_error = f"Rate limited (retry after {e.retry_after}s)"
            await asyncio.sleep(e.retry_after)
        except ToolPermissionError as e:
            # Don't retry permission errors
            return f"Permission denied: {e.message}. Cannot complete this step."
        except Exception as e:
            last_error = str(e)
            break

    return f"Tool failed after {max_retries + 1} attempts: {last_error}. Proceeding without this result."

Return the error as a string result so the agent can adapt, rather than throwing an exception that crashes the run.

Problem 3: Infinite Loops

Agents get stuck repeating the same tool calls. Usually because:

• The tool returns the same error and the agent doesn't realize it
• The agent thinks it needs more information but the tool can't provide it

Fix: Loop detection.

from collections import Counter

def detect_loop(
    recent_tool_calls: list[tuple[str, str]],  # (tool_name, args_hash)
    threshold: int = 3
) -> bool:
    counts = Counter(recent_tool_calls)
    return any(count >= threshold for count in counts.values())

# In the agent loop
tool_call_history = []

for step in range(MAX_STEPS):
    response = await claude.messages.create(...)

    for block in response.content:
        if block.type == "tool_use":
            call_signature = (block.name, hash(str(block.input)))
            tool_call_history.append(call_signature)

            if detect_loop(tool_call_history[-9:]):  # Check last 9 calls
                # Force the agent to reflect and move on
                forced_message = "You've called the same tool with the same arguments multiple times. This approach isn't working. Try a different strategy or conclude with what you know."
                # Inject this as a tool result and continue

Problem 4: Unconstrained Actions

An agent that can do anything will eventually do something you didn't intend.

Fix: Explicit action categorization and confirmation gates.

from enum import Enum

class ActionRisk(Enum):
    SAFE = "safe"           # Read operations, calculations
    LOW = "low"             # Write to local files
    MEDIUM = "medium"       # External API calls, sends
    HIGH = "high"           # Irreversible actions, deletions, financial

TOOL_RISK_MAP = {
    "search_web": ActionRisk.SAFE,
    "read_file": ActionRisk.SAFE,
    "write_file": ActionRisk.LOW,
    "send_email": ActionRisk.MEDIUM,
    "post_tweet": ActionRisk.MEDIUM,
    "delete_record": ActionRisk.HIGH,
    "process_payment": ActionRisk.HIGH,
}

async def execute_tool_with_gate(name: str, args: dict, max_auto_risk: ActionRisk) -> str:
    risk = TOOL_RISK_MAP.get(name, ActionRisk.HIGH)

    if risk.value > max_auto_risk.value:
        # Require human confirmation
        confirmed = await request_human_approval(
            f"Agent wants to {name} with args: {args}"
        )
        if not confirmed:
            return f"Action denied by user. Cannot proceed with {name}."

    return await execute_tool(name, args)

Problem 5: No Observability

You can't debug what you can't see. Production agents need structured logging.

import structlog
from dataclasses import dataclass
from datetime import datetime

log = structlog.get_logger()

@dataclass
class AgentEvent:
    session_id: str
    step: int
    event_type: str  # "tool_call" | "tool_result" | "thinking" | "complete" | "error"
    data: dict
    timestamp: datetime = field(default_factory=datetime.utcnow)

async def log_agent_event(event: AgentEvent):
    log.info(
        event.event_type,
        session_id=event.session_id,
        step=event.step,
        **event.data
    )
    # Also persist to database for post-hoc analysis
    await db.agent_event.create(data={
        "sessionId": event.session_id,
        "step": event.step,
        "type": event.event_type,
        "data": event.data,
        "timestamp": event.timestamp,
    })

With structured logging, you can:

• Replay agent sessions to debug failures
• Track which tools are called most / fail most
• Measure time spent per step
• Detect anomalous behavior patterns

The Production Checklist

Before running an agent in production:

• [ ] Maximum step count set and enforced
• [ ] Token budget set and tracked
• [ ] Loop detection implemented
• [ ] Tool failures handled gracefully (don't crash, return error strings)
• [ ] High-risk actions behind confirmation gates
• [ ] All actions logged with structured data
• [ ] Alerts configured for agent failures
• [ ] Human oversight for any irreversible action

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