GridArrayBook a readiness call
Book a readiness call

AI Production Engineering

The engineering between a working agent and a dependable system.

GridArray makes the workflow's required context trustworthy, then engineers the access, execution, evaluation, and operations required for production.

The engagement

One production outcome. Two connected engineering concerns.

Context and execution fail together. We work across both, bounded by the data, systems, and operating conditions of one real agent workflow.

01

Trusted context & access

Give the agent context it can use and access the business can govern.

We identify the sources that matter to the workflow, resolve ambiguity, and expose the required data and actions through narrow, permissioned interfaces.

Source-of-truth mappingData and document structurePermissions and provenanceRetrieval qualityAPIs and MCP tool contractsValidation and auditability
Read the agent-ready data page
02

Reliable execution & operations

Make every run measurable, recoverable, and operable.

We engineer the runtime and controls that keep multi-step agent behavior reliable when inputs, integrations, load, and failure modes become real.

Orchestration and stateRetries and failure recoveryEvaluation and release gatesObservability and replaySecurity, latency, and costIncident and operator controls
Read the production infrastructure page

From call to production

A direct path to the engineering that matters.

01

Readiness diagnostic

We inspect the workflow's data, access paths, architecture, evaluation, operational risk, and production target.

02

Focused proposal

You get a bounded plan organized around the highest-risk production gaps.

03

Senior-led engineering

We work directly in the system, in small reviewable increments, with visible evidence.

04

Operational handoff

The team leaves with governed context, an observable system, and the ability to operate and keep improving it.

The first deliverable

Your one-page production readiness scorecard.

A concrete view of where context, access, execution, and operations are ready, where risk is accumulating, and which changes unlock a safe production path.

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PRODUCTION READINESS62 / 100
ContextAt risk
AccessGap
ExecutionReady
EvaluationAt risk
OperationsAt risk

Related guides

Technical reading for the same production gaps.

Guide

AI agent data readiness checklist

A practical checklist for deciding whether the data behind one agent workflow is trustworthy enough for production use.

Guide

MCP does not fix bad data

Model Context Protocol can expose tools and resources, but it cannot repair conflicting records, missing provenance, unsafe permissions, or weak validation.

Guide

Production AI agents beyond prompts

A framework for the infrastructure a multi-step agent needs after prompting: orchestration, state, retries, evaluation, observability, security, cost, and operations.

Guide

AI agent evaluation checklist

A checklist for evaluating multi-step agent workflows before release, covering task success, context quality, tool safety, recovery, and operator visibility.

AI production readiness call

Know what stands between your agent workflow and dependable production.

A focused diagnostic across context, access, execution, evaluation, operations, and risk. You leave with a one-page readiness scorecard.

Book a readiness call