Building Reliable Agents

The demo is easy; the production agent is hard. An agent that works impressively in a demo can fail unpredictably in the real world — looping, hallucinating, taking wrong actions, or racking up huge costs — because the same flexibility that makes agents powerful makes them unreliable. Building agents that actually work in production is a discipline of managing that unreliability: adding guardrails, evaluating rigorously, and, most importantly, knowing when not to use an agent at all. This closing post is about that discipline.

By Pratik Dhanave · June 16, 2026 · 9 min read

#AI Agents #AI Engineering #LLMs #Design Patterns

This final post is about the hardest and most important part of agent development: building reliable agents — and knowing when not to build one. It covers why reliability is hard, the practical techniques for making agents more reliable, evaluation, and the crucial judgment of agents vs simpler approaches (workflows). It ties the series together with the honest, restrained guidance that runs through it: agents are powerful but costly and unreliable, so use them judiciously and engineer for reliability.

Why reliability is hard

Agent reliability is genuinely hard — the core challenge of moving agents from impressive demos to dependable production systems:

Flexibility causes unpredictability. The defining agentic property (the model decides the flow — post one) is exactly what makes agents unreliable: because the model dynamically decides actions, its behavior is non-deterministic and unpredictable — it might do the wrong thing, take an unexpected path, or fail in novel ways. The flexibility that makes agents powerful is the unpredictability that makes them hard to make reliable. You can’t have the flexibility without the unpredictability.
Errors compound over steps. In a multi-step loop, errors accumulate — a mistake early (a wrong observation, a bad decision) can derail everything after, and small error rates per step compound over many steps into a high chance of overall failure. The multi-step nature that lets agents do complex tasks also makes them fragile (more steps = more chances to go wrong). Compounding error is a fundamental agent-reliability challenge.
Failure modes are varied. Agents fail in many ways: looping (never terminating), getting stuck (repeating a failing action), hallucinating (acting on wrong information), taking wrong/harmful actions (via tools), misusing tools, going off-track, or racking up cost. This variety of failure modes makes reliability a broad challenge — many things can go wrong. (Several were flagged in earlier posts — loop termination, tool safety, self-evaluation unreliability.)
Non-determinism makes testing hard. Because agents are non-deterministic, they’re hard to test and debug — the same input can produce different behavior, and reproducing/fixing failures is harder than for deterministic code. This complicates ensuring reliability. Testing agents requires different approaches than testing deterministic systems (below).

Agent reliability is hard because the flexibility that makes agents powerful causes unpredictability, errors compound over multi-step loops, failure modes are varied, and non-determinism makes testing hard. This is the central challenge of production agents — and it’s why the discipline of reliability techniques matters. It’s also why the “use the simplest thing” guidance recurs: less agentic = more reliable.

Techniques for reliable agents

Making agents more reliable is a matter of engineering discipline — a toolkit of techniques that address the failure modes:

Constrain and guardrail. Reduce unpredictability by constraining the agent — limiting its tools to what’s needed, restricting dangerous actions (guardrails, confirmation for risky tool use, sandboxing — the tool-use post), and bounding its scope. The more constrained (less free to do arbitrary/dangerous things), the more reliable and safe. Constraints trade some flexibility for reliability — often a good trade. Don’t give an agent more freedom or power than the task needs.
Bound the loops. Always limit iterations, cost, and time (the core-loop and reflection posts) to prevent runaway loops and cost blowups. Hard bounds are basic reliability hygiene — never deploy an unbounded agent. Budgets and step limits are essential safeguards.
Ground with tools and verification. Use tools to ground the agent in real information (reducing hallucination — the tool-use post) and verification to check its work (reflection with concrete evaluation — the reflection post: tests for code, checks for results). Grounding and verification directly improve reliability by catching errors and reducing reliance on the model’s possibly-wrong internals. Verify important outputs and actions.
Handle errors gracefully. Design for failure: tools that return useful error info the agent can react to, retry logic, fallbacks, and the agent recognizing and recovering from failures. Agents will hit errors; handling them gracefully (rather than failing catastrophically or getting stuck) is key to robustness. Expect and handle failure.
Keep the agent as simple as the task allows. The most powerful reliability technique: reduce agentic complexity to what’s needed. A simpler agent (fewer tools, less freedom, more structure) or even a workflow (fixed flow — more reliable) beats an over-complex agent. Less flexibility, where you can afford it, means more reliability. This is the recurring theme: match complexity to need, and simpler is more reliable.
Human oversight where it matters. For high-stakes actions, keep a human in the loop — the agent proposes, a human approves consequential actions. Human oversight is a crucial reliability/safety mechanism for anything where an agent’s mistake would be costly. Don’t fully automate high-stakes actions without oversight.

The reliability toolkit — constrain and guardrail, bound the loops, ground with tools and verify, handle errors gracefully, keep it as simple as possible, and add human oversight for high stakes — addresses the failure modes and moves agents toward production-worthiness. Reliability is engineered in through these disciplines, not assumed. And you can’t improve what you don’t measure.

Evaluating agents

Evaluation — systematically measuring whether an agent works — is essential for reliability but harder than for traditional software, and it deserves specific attention (the blog’s “Evaluating Agents in Go” series goes deep; here the principles):

You must evaluate, not just eyeball. Because agents are non-deterministic and fail in varied ways, systematic evaluation (not just trying it a few times) is needed to know if an agent actually works reliably. Anecdotal “it worked in my demo” is not reliability evidence. Rigorous evaluation is how you gain confidence and catch regressions. Eyeballing isn’t enough.
Evaluate outcomes and trajectories. Agent evaluation looks at both outcomes (did it achieve the goal / produce the right result?) and trajectories (did it take a sensible path — right tools, no wasteful loops, good decisions?). Both matter: the right answer via a bad path may not generalize, and the path reveals reliability issues. Evaluating the process, not just the result, is important for agents.
Use test sets and repeated runs. Because of non-determinism, evaluate over sets of test cases and repeated runs (the same task multiple times) to measure reliability statistically (how often does it succeed?), not just once. This captures the variability that single runs miss. Reliability is a rate, measured over many runs and cases.
Evaluation enables improvement. Systematic evaluation lets you measure reliability, catch regressions (did a change make it worse?), and improve deliberately — turning agent development into a measurable engineering process rather than guesswork. Without evaluation, you can’t know if your agent is reliable or getting better. Evaluation is the feedback loop for building reliable agents. Invest in it.

Evaluating agents — systematically, over test sets and repeated runs, measuring both outcomes and trajectories — is essential for reliability (given non-determinism and varied failures), and it’s what turns agent development into a measurable engineering process rather than demo-driven guesswork. It’s the feedback loop for reliability. But the deepest reliability decision is upstream: whether to use an agent at all.

Agents vs simpler approaches: the key judgment

The most important reliability decision — and a fitting close to the series — is the judgment of agents vs simpler approaches, which comes back to the very first post:

The simplest approach that works is the most reliable. Reliability decreases as you move from a single LLM call → workflow (fixed flow) → agent (dynamic flow) — because more flexibility means more unpredictability. So the most reliable approach is the simplest one that solves the problem. The key judgment is choosing the least-agentic approach that suffices. Simpler is more reliable; use the simplest thing that works.
Prefer workflows when the flow can be fixed. If the task’s steps can be predetermined, a workflow (fixed flow — post one) is more reliable, cheaper, and more predictable than an agent. Reserve agents for tasks where the flow genuinely can’t be fixed (open-ended, varied, unpredictable). Many “agent” use cases are actually better as workflows. Don’t use an agent where a workflow would do. This single judgment prevents a lot of unreliability.
Match the tool to the task. The overarching principle (from post one, now with reliability weight): use an agent only when its flexibility is genuinely needed, accept the reliability cost that comes with it, and engineer for reliability (the techniques above). For everything else, use simpler, more reliable approaches. Agents are a powerful tool for a specific class of problems (open-ended, dynamic), not a universal solution. Choosing when to use them well is the meta-skill.
The honest bottom line. Agents are genuinely powerful for open-ended, dynamic, multi-step tasks that need model-directed flexibility — and genuinely unreliable, costly, and complex. Building good agent systems means using them only where warranted, engineering hard for reliability, evaluating rigorously, and defaulting to simpler approaches otherwise. That restraint and discipline — not maximal agent-ness — is what makes agents actually work. The series’ throughline: powerful pattern, used judiciously.

Building reliable agents is the discipline of managing agents’ inherent unpredictability — through constraints, guardrails, bounded loops, grounding and verification, error handling, simplicity, and human oversight, backed by rigorous evaluation — and, above all, the judgment to use agents only when their flexibility is genuinely needed, preferring simpler, more reliable approaches (workflows) otherwise. That completes the series: from what an agent is, through the loop, tools, planning, memory, reflection, and multi-agent patterns, to reliability and the judgment of when to use agents at all. Agents are a powerful pattern — most valuable when used deliberately, engineered carefully, and reserved for the problems that genuinely need them.

Key takeaways

Agent reliability is hard because the flexibility that makes agents powerful (the model decides the flow) causes unpredictability/non-determinism, errors compound over multi-step loops (small per-step error rates → high overall failure), failure modes are varied (looping, getting stuck, hallucinating, wrong/harmful actions, cost blowups), and non-determinism makes testing/debugging hard.
Engineer reliability with: constraints and guardrails (limit tools, restrict/sandbox dangerous actions, bound scope — trade flexibility for reliability), bounded loops (always limit iterations/cost/time), grounding with tools and verification (reduce hallucination, check work — tests for code), graceful error handling (retries, fallbacks, recovery), keeping the agent as simple as the task allows, and human oversight for high-stakes actions.
Evaluate agents systematically (not by eyeballing demos): measure both outcomes (did it achieve the goal?) and trajectories (did it take a sensible path?), over test sets and repeated runs (reliability is a rate given non-determinism), which catches regressions and turns agent development into a measurable engineering process.
The most important reliability judgment is agents vs simpler approaches: reliability decreases from single LLM call → workflow → agent (more flexibility = more unpredictability), so use the simplest approach that works — prefer workflows when the flow can be predetermined, and reserve agents for genuinely open-ended, dynamic, unpredictable tasks.
The honest bottom line (the series’ throughline): agents are powerful for open-ended dynamic tasks and inherently unreliable/costly/complex — so build good agent systems by using them only where warranted, engineering hard for reliability, evaluating rigorously, and defaulting to simpler approaches otherwise; restraint and discipline, not maximal agent-ness, is what makes agents work.