TL;DR
- Building a basic AI agent is incredibly easy, but making it reliable is incredibly hard.
- Reliability comes from architecture, not just the model.
- Agents need turn-based thinking: understand → act → verify → transition.
- Context maintenance prevents agents from forgetting what they learned
- 95% per-action reliability drops to 36% for 20-step tasks.
- Success requires defensive architecture and explicit verification.
Building a basic AI agent is trivially easy. Connect an LLM to tools, write a prompt, and you’ve got something that looks like it works. But put it in front of real users, and everything falls apart.
Research from institutions like MIT has found that up to 95% of AI agent proof-of-concepts fail to make it to production, often due to reliability issues that only surface when moving from demos to real-world deployment.
Between a demo agent and a production-ready one lies a deep and wide chasm. Bridging it requires understanding that reliability isn’t about the model, it’s about the architecture around it.
The Turn-Based Reality
Agents operate in turns, each requiring four steps:
- Understand state.
- Decide action.
- Execute.
- Verify outcome.
Most basic agents only handle steps 2 and 3 i.e. deciding and executing. They skip understanding and verification, which is where reliability dies.
Imagine hiring a human assistant who never confirms understanding or checks if their actions worked. That’s most agents today.
Pre-Action Checks
Before acting, agents must verify they understand the request. This sounds obvious but is routinely skipped.
Essential pre-action checks:
- State verification: Have all required information (order ID, customer details, etc.)
- Ambiguity detection: Catch multiple interpretations before acting
- “Update my shipping address” → Which order? Which address (home vs work)?
- “Book me a flight to Chicago” → Which dates? What cabin class? From which departure city?
- “Cancel my subscription” → Which subscription? Cancel immediately or at renewal?
- Prerequisite validation: Check if action is possible given current constraints
- “Cancel my order” → Verify order isn’t already shipped or delivered
- “Apply the discount code” → Check if code is valid, not expired, and meets minimum purchase requirements
- “Schedule the meeting” → Ensure attendees are available and room isn’t double-booked
- “Delete this file” → Confirm file isn’t currently locked by another process or user
- Permission boundaries: Verify authorization before taking irreversible actions
- “Refund this purchase” → Check if user has refund privileges or if amount exceeds authorization limit
- “Delete these customer records” → Verify user has admin rights and records aren’t protected by data retention policies
- “Access the financial reports” → Confirm user has appropriate role-based access for sensitive data
- “Approve this expense” → Ensure user is within their approval limit and hasn’t exceeded monthly budget
Failing fast with questions is more reliable than confidently doing the wrong thing. Users forgive questions, not mistakes.
Post-Action Verification
Knowing whether an action worked is as important as executing it. APIs return 200 status codes while operations fail, databases accept writes that get silently modified, and external services timeout leaving unknown states.
Essential post-action checks:
- Explicit success criteria: Verify outcomes, not just API responses. If you updated an email, query it back to confirm
- State consistency: After multiple actions, verify the final state matches expectations
- Rollback detection: Check if business logic silently reverted your “successful” action
- Partial failure recognition: Detect when actions only partially succeed (3 of 5 emails sent)
Turn Transitions
Between turns, agents must maintain coherent state. Two problems kill reliability:
Context degradation: Agents forget previous information, forcing users to repeat themselves and destroying trust.
Real examples:
- Customer support agent forgets the order number after 3 turns of troubleshooting, asking “What’s your order number again?” when trying to process a refund
- Travel agent loses the traveler’s frequent flyer number mid-booking, then asks for it again when trying to add loyalty benefits
- Banking assistant forgets which account the user is discussing after handling multiple transfers, requiring the user to re-specify “from my savings account, not checking”
Goal drift: Agent loses track of objectives and gets sidetracked from what users actually want.
Real examples:
- E-commerce agent trying to process a return gets stuck in payment system debugging loops instead of offering store credit or exchange options
- Calendar agent hits a scheduling conflict and starts investigating room booking system architecture rather than suggesting alternative times or locations
- IT support agent encounters a permission error and begins explaining LDAP authentication protocols instead of escalating to an admin who can approve the request
Essential transition practices:
- Explicit state tracking: Maintain structured records of what’s known, the goal, and attempts made
- Progress monitoring: Detect when spinning in place—escalate after three failed attempts
- Conversation checkpoints: Summarize key information to catch drift early
The Reliability Math Problem
Here’s why the chasm is so wide: reliability compounds exponentially, and the math is brutal.
Let’s say your agent gets each individual action right 95% of the time. That sounds pretty good, right? In isolation, it means only 1 in 20 actions fails.
But agents don’t work in isolation. They perform sequences of actions, and each action must succeed for the entire task to complete. This creates a compounding effect where overall reliability drops dramatically:
For 10 actions: 60% success rate (0.95 × 0.95 × 0.95… ten times)
- You have a 40% chance of complete failure
- More than 1 in 3 multi-step tasks will break somewhere
For 20 actions: 36% success rate
- Two-thirds of tasks fail completely
- You’re now worse than a coin flip
For 30 actions: 21% success rate
- Four out of five tasks fail
- Your “95% reliable” agent fails 80% of the time
Think about what this means in practice. A customer service agent that needs to: (1) find the customer, (2) locate their order, (3) check status, (4) identify the issue, (5) apply a solution, (6) confirm resolution, (7) update the system, (8) send confirmation—that’s already 8 actions. You’re operating at 66% reliability before any edge cases or complications.
This explains why demo agents feel functional but fail in production. Development tests simple workflows; production demands complex multi-step tasks. Your impressive 95% per-action reliability becomes a coin flip for real work.
The solution is clear: push per-action reliability toward 100%. Every check—pre-action validation, post-action verification, turn transition coherence—matters exponentially. Moving from 95% to 99% per-action reliability takes you from 36% to 82% success for 20-step tasks. That’s the difference between a broken system and a useful one.
The Reliability Mindset
Building reliable agents requires architectural thinking, not just prompt engineering. You need:
Defensive architecture: Assume LLMs will hallucinate and misunderstand. Build guardrails.
Explicit verification: Make checks first-class citizens in design—they’re the product, not overhead.
Graceful degradation: Fail safely, maintain context about failures, recover or escalate appropriately.
Observable behavior: See exactly what the agent is thinking at each turn. Logging is essential for debugging reliability issues.
Crossing the Chasm
Teams that succeed realize: an agent isn’t an LLM with tools—it’s a system where the LLM is one component, and reliability comes from everything around it.
Your architecture needs turn-based thinking from day one:
- Pre-action: understand and validate
- Action: execute with error handling
- Post-action: verify and confirm
- Transition: maintain context and track progress
Each turn is a contract: understand before acting, verify after acting, carry forward context truthfully. Break the contract, lose trust. Maintain it consistently, build something reliable.
The chasm is wide but not impassable. Reliability is earned through architectural discipline, not prompt engineering magic. Build systems that check themselves. Build agents that know when they don’t know. Build for the real world, not the demo.
Building agents is easy. Building ones that work 10,000 times in a row? That’s the real challenge.