How to Build a Reliable Agent

Your AI agents are already making decisions. They approve refunds, trigger workflows, and interact directly with production systems.

This guide is written for developers and teams building agents that must operate safely in production environments. It presents a practical three-step framework for building reliable agents:

• Evaluate your agent responses systematically to uncover failure modes.
• Structure decision context using a Context Graph so the right information is available at decision time.
• Enforce every action through a deterministic Decision Runtime before execution.

By the end of this guide, you will understand how to design agents that do not simply generate decisions, but validate and prove them before they are allowed to run.

Step 1: Evaluate Before You Optimize

1.1 Generate Agent Evals set Automatically

Start by generating tests automatically.

Why? Because you cannot manually anticipate every scenario your users will trigger.

Agents operate in open environments. Humans are unpredictable. Edge cases are the norm.

Generate evaluation scenarios from:

• Policies
• Documentation
• Knowledge bases
• Historical tickets
• Structured datasets

The objective is to get as close as possible to 100% domain coverage.

This means generating:

• Boundary conditions (just below or just above thresholds)
• Conditional branches
• Missing-field scenarios
• Contradictory inputs
• Escalation triggers

If you are using a tool like Rippletide’s evaluation framework (see evaluation overview), you can automate Q&A generation and run evaluation against your endpoint. But the key principle is independent of tooling.

The metric to track is coverage and predictability, because production failures live in the long tail.

1.2 Leverage Business Domain Expertise (Including Unwritten Rules)

At this stage the agent is not running yet, so the tests that can be performed cover only what is written down.

But real reliability comes from encoding what the organization knows but never wrote down.

Every company has unwritten guidelines:

• “If the amount is close to $25, get a second pair of eyes.”
• “If the request involves enterprise contracts, route to legal.”
• “If this partner is involved, double-check manually.”

These rules rarely appear in policy PDFs or systems of record.

They live in manager habits and in how teams operate naturally.

Your agent will not infer them automatically before it has run in a real environment.

If you do not encode them, it will optimize for literal compliance, not operational judgment.

Encode those scenarios explicitly in your evaluation suite.

For example:

• A refund under the threshold, but repeated suspicious requests.
• A valid transaction, but customer tone indicates fraud risk.
• A compliant request, but unusual timing or geography.

These are not edge cases. They are cultural safeguards.

They represent how the business actually operates.

Automatic tests explore documented rules. Domain-informed tests capture institutional reflexes.

Both are required.

Because agents do not just need to follow policy. They need to behave like your best operator would.

And that operator applies judgment beyond written rules.

1.3 Runtime Filtering: Because You Cannot Predict Every Interaction

Even with high coverage and explicit guarantees, one fact remains:

Human behavior is not enumerable. Humans act through a long-tail distribution. In a sales inbound chatbot, people ask very diverse questions, each with very low probability. There is always something that comes up that was not anticipated.

This is where runtime filtering becomes essential.

Runtime filtering means:

• Detecting incomplete or ambiguous inputs
• Rejecting actions when required fields are missing
• Blocking execution when confidence is insufficient
• Escalating when policy validation cannot be deterministically confirmed

Evaluation protects you before deployment.

Runtime filtering protects you after deployment.

It acts as a safety net for the unknown unknowns.

Because no test suite, no matter how broad, captures the full entropy of human input.

You can try Rippletide hallucinations and guardrail filters here.

Step 2: Structure and Serve Context with a Context Graph

Evaluation shows you where your agent fails. A Context Graph addresses why it fails.

Many production errors are context errors, because the right data is not present in the context window to operate.

Agents today typically reason over a mixture of prompts, retrieved documents, tool outputs, and raw JSON. The model is then expected to infer structure from text and find relations between each element.

This approach is fragile because production decisions depend on relationships, constraints, and state transitions. They should not depend on how well the model interprets a paragraph.

A Context Graph makes those relationships explicit, surfaces inconsistencies, highlights incoherent data, and reveals missing context to avoid letting a model guess between contradictory facts.

You can either build those manually or use products such as Rippletide automatic ontologies.

Here is the way if you want to do it manually:

2.1 Identify the Real Entities in Your Domain

Start by identifying the core entities your agent interacts with.

In a refund workflow, these might include Customer, RefundRequest, RefundPolicy, Transaction, and ApprovalThreshold.

Each entity becomes a node in the graph.

Then define how they relate to each other. A RefundRequest belongs to a Customer. A RefundRequest has an amount. A Customer has a verification status. A RefundPolicy defines limits and thresholds.

Once your system operates on facts instead of paragraphs, ambiguity decreases immediately.

2.2 Represent Policies as Constraints, Not Text

Policies are often written as documents. Instead, translate policies into constraints over state.

For example, if the refund amount is less than or equal to the automatic approval limit, the request may be approved. If the amount exceeds that limit but remains below the maximum threshold, the request must be escalated. If it exceeds the maximum threshold, it must be rejected.

When the system evaluates numeric values against defined constraints, the outcome becomes deterministic.

You are no longer asking the model to interpret language. You are asking it to evaluate structured conditions.

2.3 Encode Time and Source of Truth

Real systems evolve and policies should be versioned. For example, customer data changes and verification expires.

Your graph should include timestamps and source metadata. For example, a Customer identity status should include when it was verified. A RefundPolicy should include its version. Each data point should reference its source system.

This allows you to reconstruct exactly which state produced a decision.

Traceability is then created by default and stored, and it enables you to replay scenarios afterward to improve an agent.

2.4 Query Before the Agent Decides

Before the agent proposes an action, it should retrieve the relevant entities from the graph.

Instead of passing long prompts with mixed instructions and data, you fetch structured state.

The model reasons over defined entities and constraints, not over loosely connected text.

This reduces hallucination risk and increases consistency.

It also makes missing information visible. If a required node is absent, the system can block or escalate before execution.

2.5 What Changes After Structuring Context

Once context is structured, decisions become reproducible.

Contradictions no longer hide inside wording, and missing data becomes explicit. Escalation logic becomes enforceable and versioning becomes auditable.

You stop asking why the model answered a certain way.

You start inspecting which entities and constraints led to the result.

Evaluation exposed your weaknesses.

The Context Graph removes ambiguity from the reasoning environment.

The final step is to ensure that every decision is enforced before it touches the real world.

Step 3: Add a Decision Runtime

Evaluation tells you if something is correct. A context graph structures what is true.

The next step is to control execution.

A Decision Runtime sits between intent and action.

Its role is simple:

• Validate policy constraints
• Check graph consistency
• Prevent unsafe actions
• Freeze validated trajectories

This is where non-regression begins.

If an action pattern has been validated for a parameter set, you freeze it.

If a new trajectory emerges:

• Simulate
• Evaluate
• Compare against existing trajectories
• Promote only if strictly better

This introduces macro-determinism. Micro-level exploration is allowed and macro-level guarantees are enforced.

Contact Rippletide if you would like to try this module.

What You Should See After 2 Weeks

If implemented correctly, you should observe:

• Measurable reduction in hallucinations, you should be able to reach less than 2% using such methods, dividing current agentic rates by roughly 7 to 8x
• Stable refusal behavior
• Fewer regressions after updates
• Explainable decisions, 100% is possible once you enforce a decision runtime
• Faster iteration cycles

Most teams optimize prompts, but top teams optimize infrastructure.