Reliable Agents: Engineering Reliable AI Agents

The promise of agentic AI has always been clear: create autonomous systems that can reason, plan, and execute complex tasks. But the reality has been messy. Most large language models, trained for conversation, are unreliable in practice. They hallucinate tool calls, generate malformed data, and fail to follow precise constraints. They can talk about the work, but they often can’t do the work.

How do we build agents that execute reliably? The answer lies not in scaling conversational ability, but in systematically engineering specific, verifiable skills. By examining the methodologies behind two distinct and powerful models—Nous Research’s Hermes 4 and OpenAI’s GPT-5-Codex—we can extract a clear blueprint for building the next generation of reliable agents.

Principle 1: Engineer for Verifiable Reasoning

An agent’s thought process should not be a black box. To be reliable, its reasoning must be explicit, inspectable, and controllable.

• Hermes 4 was explicitly trained to externalize its reasoning using <think> tags. This creates a step-by-step rationale that a developer can use to debug a failing workflow or steer the agent’s logic. Crucially, the Hermes team also engineered a ‘thinking budget,’ training the model to emit a </think> token after a set length. This gives developers a direct mechanism to control computational resources and prevent runaway costs in autonomous loops.

• GPT-5-Codex evolves this concept into a dynamic, task-aware system. It adapts its reasoning time based on a task’s complexity, feeling snappy on simple requests while working for hours on large-scale refactors. It iterates on its own implementation, fixes test failures, and persists through complexity. This is a more sophisticated form of engineered reasoning—one that manages its own cognitive resources to see a task through to completion.

The takeaway: Reliable agents are trained not just to think, but to expose their thinking in a structured way. This allows for debugging, steering, and resource management, which are essential for production systems.

Principle 2: Ensure Reliable Tool Use and Schema Adherence

An agent is useless if it cannot interact with its environment predictably. This requires rigorous training on using tools correctly and adhering to strict data formats.

• Hermes 4 was trained specifically to produce syntactically correct JSON for tool calls that adhered to predefined schemas. The team went a step further by training it on an ‘editing’ task: given malformed JSON, the model had to identify and correct the validation errors. This builds an agent that is not just a tool user, but a reliable, parsable component in a larger system.

• GPT-5-Codex demonstrates this skill in a live, high-stakes software engineering environment. It is purpose-built for developer tools where exactness is non-negotiable. Its capabilities extend beyond text, using visual inputs like screenshots to understand a task. It can then visually inspect the frontend code it produces, closing the loop and verifying its work against a visual schema.

The takeaway: Don’t just teach an agent about tools. Train it on correctly formatted calls, schema adherence, and even self-correction. The goal is to make its output programmatically verifiable.

Principle 3: Enforce Rigorous Instruction and Constraint Following

Agents often operate under a complex set of rules. Their ability to follow these constraints without deviation is a core measure of their reliability.

• Hermes 4 was trained using benchmarks with specific, verifiable instructions, such as, ‘Every Nth word of your response must be in French.’ By training on thousands of these trajectories, the model learns to respect and execute complex, multi-part instructions. This moves beyond simple prompt-following to a more rigorous form of logical adherence.

• GPT-5-Codex applies this principle to the ultimate test: code review. This is not just about following stylistic rules; it is a deep, verifiable execution of intent. The agent reasons over an entire codebase, runs tests to validate correctness, and adheres to formal specifications like AGENTS.md. It actively verifies that its output achieves the developer’s stated goal within a given operational framework.

The takeaway: To build agents for high-stakes or precision-dependent tasks, train them on datasets that include complex, verifiable constraints. Reliability is a direct function of the model’s proven ability to follow rules.

A Blueprint for Building Better Agents

Hermes 4 and GPT-5-Codex, despite their different origins and scales, point to the same conclusion: reliable agents are not found, they are built. They are products of a deliberate engineering process that prioritizes verifiable skills over conversational flair.

For anyone building agentic systems, the path forward is clear. Focus your efforts on engineering these three core principles: create models with inspectable reasoning, train them for rigorous tool use and schema adherence, and test them against complex, verifiable constraints. That is how you build an agent that gets the job done.