When Agents Fail: Anatomy of Problems in Generative Systems

In recent months, generative agents have moved from being a technical curiosity to occupying a real place in the architecture of many systems. More and more companies are integrating them to coordinate flows, respond to customers, or execute complex tasks without human intervention.

But behind that promise of "intelligent" automation lies an uncomfortable fact: agents fail, and they do so in ways that are difficult to anticipate.

Most guides talk about how to make them resilient. However, before designing resilience, we need to understand where they break. This text does not seek to show best practices, but rather to explore the most common weak points, those that tend to appear when an agent stops behaving as we expect.

A generative agent is not just a model that responds. It is a chain of dependencies ranging from the base model to the tools it uses. Each layer is a possible source of error.

The foundational model can be the first crack. Sometimes the problem lies not in its capacity, but in its context. When token limits are exceeded or instructions are combined confusingly, the model starts to "invent" steps or repeat phrases. In production, this translates into erroneous decisions and conversations that drift without reason.

Orchestration is another sensitive point. An error in state management, a poorly passed variable, or an ill-defined condition can cause infinite loops or incoherent responses. Many visible agent failures do not come from the model, but from how we direct it.

Infrastructure also bears its share of responsibility. High latencies, expiring sessions, or systems that cannot support the real concurrency of daily use. When an agent depends on multiple distributed components, a small delay is enough to break the synchrony of the entire flow.

The knowledge base (KB) is often a silent source of errors. Poor indexing, failure to filter old versions, or lack of control over content relevance causes the agent to respond with imprecise information. And the worst part: it does so with confidence.

External tools amplify the risk. An API that changes its response, a function that does not return an explicit error, or an uncontrolled timeout can freeze the entire execution. When the agent does not have a Plan B, the conversation stalls or restarts without warning.

Security and compliance are also vulnerable. Prompt injections or the exposure of sensitive data in logs happen more often than admitted. It is not just about intentional attacks, but about oversights in how contexts are stored and tracked.

Observability closes the list. Many teams fail to identify a failure because the system does not log enough. Without clear metrics or semantic traces, the error repeats itself without leaving a trace.

In practice, most incidents fall into five structural patterns.

Shared Dependency. When all flows depend on the same service or component (for example, the vector database), any interruption affects the entire system. It is a single point of failure, and it almost always goes unnoticed until it happens.

Insufficient Capacity. Many agents work well in test environments but collapse when scaling. It is not just an issue of CPU or memory: it is also about token limits, context size, and call queue saturation.

Growing Latency. There isn't always a technical failure. Sometimes the system simply stops being useful because it takes too long. Users abandon before the agent finishes reasoning.

Fragile Dependencies. Agents depend on external services: translators, search engines, vector databases, email services. When one of these pieces changes or fails, the agent has no way to recover.

Behavioral Drift. This is the hardest to detect. The model starts ignoring rules, forgetting steps, or responding with a different tone. It happens slowly, as the context fills up or instructions lose weight.

Imagine an agent that depends on an API to check prices. One day, that API changes a field in its response. The agent, expecting a previous format, misinterprets the data. The result is a failed call that generates no error and attempts to retry. In seconds, the system enters a silent loop that consumes tokens, increases costs, and blocks other tasks.

It all started with a minor change, but the lack of isolation between components caused the failure to propagate.

In most cases, agents don't fail because they are “unintelligent,” but because they lack clear boundaries between stages. Every unhandled error becomes a snowball.

There are signs that appear long before a complete failure. Latencies that rise slowly, truncated responses, empty logs, or semantic repetitions (“Trying to resolve…” over and over).

Monitoring these signals requires more than technical metrics: it requires semantic observability, the ability to understand if the agent is thinking or just repeating itself.

Controlled chaos also helps. Tests where network errors, corrupt responses, or forced timeouts are simulated. If the agent cannot recover from that, it probably won't in production either.

Resilience is not added at the end of development. It is designed from the start, assuming that every component will fail at some point.

That implies building isolation between modules, adding explicit validations, and defining recovery behaviors. But above all, it implies accepting that agents are not deterministic systems: they think with uncertainty, and that demands an architecture that knows how to live with it.

A generative agent does not fail for lack of reasoning, but for lack of foreseeing the error.

And understanding its failures is the first step to designing agents that can truly sustain themselves in the real world.