How AI Agents Handle Errors and Uncertainty

Infrastructure Error Handling

Infrastructure errors occur at the system level, below the agent reasoning process. The most common are API timeouts (the model or tool provider does not respond within the expected time), rate limits (too many requests in a given period), network errors (connection failures, DNS resolution problems), and malformed responses (the API returns data that does not match the expected format).

The agent runtime handles infrastructure errors before they reach the model. Retry logic with exponential backoff automatically retries failed requests with increasing delays between attempts. A first retry might wait one second, a second retry four seconds, a third retry sixteen seconds. This pattern prevents overwhelming a struggling service while still recovering from transient failures. Most transient errors resolve within three to five retries.

Circuit breakers prevent cascading failures when a service is consistently down. After a configurable number of consecutive failures (typically three to five), the circuit breaker opens and immediately rejects subsequent requests to that service without attempting them. After a cooling period (typically 30 to 60 seconds), the circuit breaker allows one test request through. If the test succeeds, the circuit closes and normal operation resumes. If the test fails, the circuit stays open for another cooling period. This pattern prevents the agent from wasting resources on a service that is clearly unavailable.

Timeout management sets maximum wait times for model calls, tool executions, and overall task completion. Without timeouts, a single slow operation can block the agent indefinitely. With timeouts, the agent can detect stalled operations and take corrective action: trying an alternative approach, skipping the blocked step, or reporting the issue to a human operator.

Semantic Error Handling

Semantic errors are errors in meaning rather than infrastructure. The tool executed successfully but returned wrong data. The agent followed a valid strategy but it did not produce the expected result. The user question was ambiguous, and the agent interpreted it incorrectly. These errors require the agent to reason about what went wrong and how to fix it.

Self-correction is the agent ability to detect its own mistakes and fix them. When a tool returns unexpected results, the agent can compare the results against its expectations, identify the discrepancy, and try a different approach. A web search that returns irrelevant results might prompt the agent to refine the search query. A database query that returns no rows might prompt the agent to check whether the search criteria are correct.

Self-correction works because the model can reason about error messages and unexpected outputs. When a tool returns "Error: User not found," the model can infer that the user identifier was wrong and try a different identifier. When a calculation produces a result that seems unreasonable (a company revenue of negative five billion dollars), the model can question its inputs and verify them before presenting the result. This reasoning ability is what makes LLM-based agents more resilient than traditional automation, which can only follow pre-programmed error handling scripts.

Validation checks before and after tool calls catch errors that the model might not notice. Pre-execution validation verifies that tool parameters are reasonable (dates are not in the future for historical queries, amounts are positive, email addresses match expected formats). Post-execution validation checks that tool results are plausible and consistent with prior results. These programmatic checks complement the model reasoning, catching errors that the model might overlook.

Fallback Strategies

Fallback strategies define alternative approaches when the primary approach fails. They are designed in advance for every critical operation, ensuring that the agent has options when things go wrong. Good fallback strategies are ordered by preference, with the most desirable alternative first and progressively less desirable alternatives after.

Data retrieval fallbacks might proceed like this: try the primary API, try the secondary API, try the cached data, try a web search, report that the data is unavailable. Each fallback is less ideal than the previous one (the secondary API might have slightly different data, cached data might be outdated, a web search might return approximate answers), but any of them is better than complete failure.

Action fallbacks handle situations where the agent cannot perform the requested action. If the email service is down, queue the email for later delivery. If the database is locked, wait and retry. If the user file system is full, compress existing files before writing new ones. These fallbacks keep the task moving forward even when the primary execution path is blocked.

Uncertainty Management

AI agents operate under constant uncertainty. The model is not certain that its reasoning is correct. Tool results might be incomplete or outdated. User intent might be ambiguous. Production agents need strategies for managing this uncertainty rather than pretending it does not exist.

Confidence calibration helps agents distinguish between situations where they are likely right and situations where they are uncertain. When the agent is confident (clear instructions, unambiguous data, familiar task type), it can act quickly. When the agent is uncertain (ambiguous instructions, conflicting data, unfamiliar task type), it should slow down, gather more information, or ask for human guidance.

Verification loops add a check step after critical decisions. Before sending an email, verify the recipient address. Before deleting records, verify the selection criteria. Before publishing content, verify the formatting and accuracy. These checks add a small amount of latency but prevent costly mistakes that are difficult or impossible to undo.

Escalation to Humans

The final error handling strategy is escalation: asking a human for help. Escalation is not a failure of the agent. It is a designed behavior that recognizes the limits of automated decision-making. The best agents escalate early and clearly, providing the human with complete context about what they tried, what went wrong, and what options they see.

Effective escalation includes the original task description, a summary of actions taken so far, the specific error or uncertainty that triggered escalation, intermediate results that might be useful, and suggested next steps for the human to consider. This context lets the human make an informed decision quickly rather than having to reconstruct the entire situation from scratch.

Escalation should be configurable by task type and risk level. Low-risk, routine tasks might never escalate, handling all errors automatically. High-risk tasks involving financial transactions, legal documents, or production systems might escalate at the first sign of uncertainty. The appropriate escalation threshold depends on the cost of errors, the frequency of errors, and the availability of human operators to handle escalated tasks.

Error Budgets and Quality Targets

Error budgets define acceptable error rates for different categories of agent operations. A customer-facing agent might have an error budget of 2 percent for factual accuracy, 5 percent for formatting issues, and 0.1 percent for data privacy violations. These budgets acknowledge that perfection is unattainable while setting clear standards for what is acceptable. When the error rate for any category exceeds its budget, the team prioritizes fixing that category over other improvements.

Quality targets go beyond error avoidance to define what good looks like. A customer support agent might have quality targets for response relevance (the answer addresses the actual question), completeness (the answer covers all aspects of the question), clarity (the answer is easy to understand), and actionability (the answer tells the user what to do next). These targets are measured through human evaluation and model-as-judge scoring, with periodic calibration to ensure consistency.

Error classification helps prioritize improvement efforts. Not all errors are equally important. A cosmetic error (slightly awkward phrasing) is less important than a functional error (wrong answer) which is less important than a safety error (leaking private data). Classifying errors by severity ensures that the most impactful errors are addressed first, even if they are less frequent than minor issues.