Why Most AI Agents Fail in Production

Everyone's building AI agents now. Every startup pitch deck has "AI agents" somewhere. Every enterprise is experimenting with "autonomous workflows."

Most of them will fail. Not because the LLMs aren't good enough — they are. But because people fundamentally misunderstand what it takes to run agents in production.

I've been building an agent orchestration platform. Here's what I've learned.

The Demo vs Production Gap

Demo agent: "Ask it anything, watch it figure out the answer!"

Production agent: Needs to handle edge cases, not hallucinate, stay within budget, be auditable, recover from failures, and work reliably thousands of times a day.

These are completely different problems.

The demo optimizes for "wow." Production optimizes for "boring reliability." Most teams never make the transition.

Problem 1: The LLM Decides Everything

The biggest mistake: letting the LLM control the flow.

"Here are some tools. Figure out what to do."

This works in demos. In production, it means:

• Unpredictable execution paths
• No cost predictability (might call 3 tools or 30)
• Hard to debug when things go wrong
• Inconsistent behavior across runs

The insight that changed everything for me: separate control flow from reasoning.

The workflow decides what steps run in what order. The LLM reasons within bounded steps. (I wrote more about this in Why Your AI Needs Deterministic Workflows.)

BAD:  LLM → decides everything → unpredictable chaos
GOOD: Workflow → bounded LLM steps → deterministic paths with flexible reasoning

The LLM is powerful. But power without constraints is dangerous in production.

Problem 2: No Memory Architecture

Most agents have no memory. Every conversation starts fresh. Or they dump the entire conversation history into context, which gets expensive and eventually overflows.

Better approach: distinguish between different types of memory. (I go deeper on this in Building AI That Actually Remembers.)

Short-term: Current conversation. Recent messages.

Episodic: What happened in past sessions. "Remember when we debugged that auth issue?"

Semantic: Facts and entities. Structured knowledge with provenance.

Procedural: Instructions, policies, preferences. The rules the agent follows.

These require different storage, different retrieval, different update patterns. Treating them all as "just throw it in a vector database" doesn't work.

Problem 3: No Cost Controls

LLMs are expensive. An uncontrolled agent can burn through hundreds of dollars in minutes.

I've seen it happen:

• Infinite loop calling tools
• Recursive reasoning that never terminates
• Context windows ballooning with each iteration

You need hard limits:

• Maximum iterations per execution
• Maximum tool calls per step
• Token budgets per workflow
• Timeouts that actually kill execution

These aren't optional. They're survival. (More on this in The Cost Problem in AI Nobody Talks About.)

Problem 4: Can't Debug When It Fails

Agent does something wrong. Customer complains. You ask: "What happened?"

If you can't answer that question in 5 minutes, your agent isn't production-ready.

Requirements:

• Full execution trace (what ran, in what order, with what inputs/outputs)
• Decision logging (why did it choose path A over path B?)
• Reproducibility (can you replay this execution?)
• Cost tracking (how much did this execution cost?)

Most agent frameworks give you none of this. You're flying blind.

Problem 5: Treating Agents Like Features

"Let's add an AI agent to handle customer support."

Cool. Did you think about:

• What permissions does it have?
• What data can it access?
• Who reviews its outputs before they reach customers?
• What happens when it's wrong?
• How do you update its behavior without redeploying?
• Who's responsible when it makes a mistake?

Agents aren't features. They're team members. They need identity, permissions, governance, and oversight. (I explore this mental model in Agents as Colleagues, Not Features.)

What Actually Works

After building this for a while, here's what I've found works:

1. Deterministic Workflows with LLM Nodes

The workflow is a graph. Nodes can be:

• LLM reasoning (with tool access)
• Conditional routing
• Human approval gates
• External API calls
• Data transformations

The graph is fixed. The LLM operates within nodes. You get predictability + flexibility.

2. Bounded Execution

Every execution has limits:

• Max 25 node visits total
• Max 10 visits per node
• 5-minute timeout
• Token ceiling

If limits are hit, execution stops gracefully. You'd rather fail predictably than succeed unpredictably.

3. Checkpointing

After each node completes, checkpoint the state. If execution crashes, resume from checkpoint.

Hot checkpoints in Redis (active executions). Cold storage in your database (historical).

Crash recovery isn't optional for production systems.

4. Observable Everything

Every execution produces:

• Full trace with timing
• Input/output for each node
• Decision rationale
• Cost breakdown
• Error context if failed

When something goes wrong, you can see exactly what happened.

5. Human-in-the-Loop

Some decisions shouldn't be fully automated:

• High-value transactions
• Customer-facing communications
• Irreversible actions

Build approval gates into your workflows. Route sensitive decisions to humans. The agent proposes, the human approves.

The Architecture That Emerged

After iterating on this, here's roughly what the stack looks like:

API Layer (request handling, auth, streaming)
    ↓
Worker Layer (async execution, background jobs)
    ↓
Workflow Engine (DAG execution with cycle support)
    ↓
Intelligence Layer (LLM calls, tool execution, RAG)
    ↓
Storage (relational + graph + vector, unified)

Each layer has clear responsibilities. The workflow engine handles execution logic. The intelligence layer handles AI. They don't bleed into each other.

The Hard Part Nobody Talks About

Building the agent is maybe 20% of the work.

The other 80%:

• Observability and debugging tools
• Cost tracking and limits
• Permission systems
• Error handling and recovery
• Testing (how do you test non-deterministic systems?)
• Updating behavior without breaking things
• Audit trails for compliance

These aren't exciting. They're essential.

AI agents are powerful. But "powerful" and "production-ready" are different things. The teams that succeed will be the ones who treat agents as serious infrastructure, not magic demos.