Production AI Agents: Deterministic Tooling, Evals, and Structured Workflows
How to engineer reliable, deterministic agentic workflows that do not hallucinate or crash.
“How do we make LLM agents reliable enough for enterprise production?”
Most AI agent demos collapse the moment they encounter real-world ambiguity, rate limits, or unexpected outputs. Engineering production-ready agents requires treating LLMs as probabilistic calculation units inside a deterministic, strongly typed software harness.
The Demo vs. Production Gap in AI Agents
Building an agent demo that passes a single test prompt takes fifteen minutes. Building an agent that executes multi-step tasks across thousands of real enterprise users with a 99.5%+ completion rate is one of the hardest software engineering challenges in the industry today. Unconstrained natural language generation inevitably leads to invalid tool calls, circular reasoning loops, and catastrophic drift.
Strict Type Boundaries via JSON Schemas
The primary defense against agent failure is eliminating raw string parsing. Every tool provided to an agent must be defined using a strict JSON Schema or Zod validator with exhaustive property descriptions. The model must never be allowed to emit freeform text when a structured decision is expected; responses must parse cleanly into typed objects before execution.
import { z } from 'zod';
export const QueryCustomerRecordsTool = {
name: 'query_customer_records',
description: 'Search customer database by verified organization ID and status filter.',
parameters: z.object({
organizationId: z.string().uuid({ message: 'Must be a valid UUID' }),
status: z.enum(['active', 'trial', 'churned']),
limit: z.number().int().min(1).max(50).default(10),
}),
execute: async (args: z.infer<typeof QueryCustomerRecordsTool.parameters>) => {
try {
const records = await db.customer.findMany({
where: { orgId: args.organizationId, status: args.status },
take: args.limit,
select: { id: true, name: true, plan: true, mrr: true }
});
return { success: true, count: records.length, data: records };
} catch (error) {
// Return structured error context so the model can self-correct
return { success: false, error: (error as Error).message, recoverable: true };
}
}
};Bounded Execution Loops and Self-Correction
Agents must never run in infinite `while(true)` loops. We enforce strict step budgets (e.g., maximum 8 tool invocations per task), token cost ceilings, and execution timeouts. If a tool returns an error, the error is piped back into the conversation context as a structured observation, allowing the model to self-correct its parameters on the next turn.
The Indispensable Role of Automated Evals
You cannot optimize what you do not measure. In every AI product we engineer at Scarif Labs, we establish automated evaluation test suites before writing application code. Every model prompt revision is tested against a benchmark of golden test cases, grading execution accuracy, tool invocation correctness, and cost per task.
- 01.Treat language models as probabilistic components wrapped inside deterministic type-safe harnesses.
- 02.Define all tool inputs and outputs with strict Zod/JSON schemas; never rely on regex text extraction.
- 03.Enforce hard execution bounds, token ceilings, and automated evaluation suites to guarantee production reliability.
Applied in our production systems & services
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