What is the Reliability Chasm in AI Agents?
The reliability chasm refers to the massive gap between building a demo AI agent that appears to work and creating a production-ready agent that operates reliably at scale. While basic AI agents are easy to build with simple LLM-and-tool combinations, achieving consistent reliability requires sophisticated architectural patterns including pre-action checks, post-action verification, turn-based context management, and defensive design. A demo agent with 95% per-action reliability drops to just 36% success for 20-step tasks.
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.
Frequently Asked Questions
Why do AI agents work in demos but fail in production?
Demo agents typically follow simple happy-path scenarios with clear inputs and expected outputs. Production environments present edge cases, ambiguous user requests, system failures, and evolving contexts that demos never encounter. Additionally, demo reliability of 95% per action sounds good but means 80% failure for 20-step tasks—exposing why most agents fail when moved from controlled demos to real-world complexity.
What are pre-action checks and why do they matter?
Pre-action checks are verification steps an agent performs before taking any action. They include state verification (confirming all required information exists), ambiguity detection (catching multiple interpretations), prerequisite validation (checking if the action is possible), and permission boundary verification (confirming authorization). Failing fast with questions is more reliable than confidently taking wrong actions.
How does context management affect agent reliability?
Context degradation occurs when agents forget previous information, forcing users to repeat themselves and destroying trust. Goal drift happens when agents lose track of objectives and get sidetracked. Reliable agents maintain explicit state tracking, progress monitoring, and conversation checkpoints to preserve context across turns. As explored in agentic workflows, structured context management is fundamental to reliability.
What is the reliability math problem in multi-agent systems?
Reliability compounds exponentially across actions. An agent with 95% per-action reliability has only 60% success for 10-action tasks, 36% for 20-action tasks, and 21% for 30-action tasks. This explains why impressive demo agents fail in production—real workflows require many actions, and each additional action dramatically increases failure probability. Moving from 95% to 99% per-action reliability takes 20-step tasks from 36% to 82% success.
How do post-action verification steps work?
Post-action verification confirms that actions actually worked as intended. This includes explicit success criteria (querying back to verify updates), state consistency checks (confirming final state matches expectations), rollback detection (catching business logic that silently reverted changes), and partial failure recognition (detecting when 3 of 5 emails sent). APIs return 200 status codes while operations fail, so verification is essential.
What is defensive architecture in AI agents?
Defensive architecture assumes LLMs will hallucinate and misunderstand, then builds guardrails accordingly. This includes explicit verification as first-class design elements, graceful degradation with safe failure modes, observable behavior through comprehensive logging, and human escalation thresholds. The architecture makes reliability a systemic property rather than relying on the model to always be correct.
How can I improve my agent’s reliability?
Start by implementing turn-based thinking: understand before acting, verify after acting, maintain context between turns. Add pre-action checks for state, ambiguity, prerequisites, and permissions. Implement post-action verification with explicit success criteria. Build defensive architecture that assumes failures will happen. Use structured workflows as described in workflow patterns to create reliable agent systems.
About the Author
Vinci Rufus is a technologist and writer focused on building reliable AI systems and agent architectures. He writes about the practical challenges of production AI implementations and the architectural patterns that separate demo agents from production-ready systems. His work covers thinking in agents, workflow design, and the infrastructure needed for dependable agentic applications.