AI AgentsHafsteinn Runarsson · AI Konsulent11 Aug 2026 · 8 min

Building production-ready AI agents: a guide

A useful demo proves that an agent can complete a task once. Production asks a harder question: can the system do the right work repeatedly, stop safely when it cannot, and leave enough evidence for someone to understand what happened?

That shift changes the job. The model still matters, but it is only one part of the product. A production-ready AI agent also needs clear boundaries, reliable tools, controlled access, evaluation, monitoring, and a recovery path.

This guide covers the decisions to make before an agent handles real work.

Start with a bounded job

"Build an AI agent" is too broad to guide a production system. Give the agent a job with a visible start, a defined finish, and an owner.

A workable job definition answers:

• What starts the run?
• What information may the agent read?
• Which actions may it take?
• What counts as a completed result?
• When must it stop or ask for help?
• Who owns the result after the run?

Take inbox triage as an example. "Manage email" is open-ended. "Classify new support messages, draft a reply from approved account data, and route uncertain cases to a person" is bounded. The second version gives you something you can test and review.

Start with one job. Add adjacent work only after the original boundary holds under normal use and failure conditions.

Separate reasoning from authority

An agent may be able to propose an action without having permission to execute it. Keep those two capabilities separate.

For each tool, decide whether the agent may:

• read information;
• prepare a change;
• execute a reversible action;
• execute an irreversible or externally visible action.

A draft, preview, or queued action is often a sensible first production boundary. It lets the agent do useful work while a person retains control over publication, payment, deletion, access changes, or other sensitive steps.

Approval should be specific. The reviewer needs to see what will happen, which data or account is affected, and whether the proposed action changed after review. A generic approval such as "let the agent continue" is difficult to audit.

Design tools as narrow contracts

Agent tools should expose business actions, not unrestricted access to an underlying system.

Prefer a tool such as create_support_reply_draft(ticket_id, body) over a generic database or shell interface. The narrow tool can validate its inputs, enforce permissions, reject protected records, and return a result the agent can understand.

Each tool contract should define:

• required and optional inputs;
• allowed values and limits;
• the identity used for authorization;
• success and error responses;
• whether the action is idempotent;
• which events must be logged.

Treat tool output as untrusted input. A document, web page, message, or API response may contain instructions that do not belong to the agent's task. Keep system policy and user intent above retrieved content, and pass only the fields the next step needs.

Make state explicit

Production agents often span several steps. Do not hide the run state inside a long conversation transcript.

Store the parts that matter as structured state: the goal, current step, source records, decisions, approvals, tool results, retry count, and final status. This makes it possible to resume a run without replaying every token and to inspect the exact point where it stopped.

Use clear terminal states such as completed, failed, cancelled, and needs review. A run that merely stopped producing output is not a useful status.

Long-lived memory needs its own boundary. Save durable facts only when the user or product has a reason to retain them. Keep temporary plans, intermediate tool output, and sensitive data out of persistent memory unless the workflow requires them.

Build the failure path first

The happy path is usually easy to demonstrate. Production work is defined by what happens when a dependency is slow, data is missing, a tool returns an unexpected shape, or the requested action conflicts with policy.

For every step, decide:

1. Which errors may be retried?
2. How many attempts are allowed?
3. Could a retry repeat a side effect?
4. What evidence should be preserved?
5. When does the run move to human review?

Retries need an idempotency strategy. If a network timeout occurs after an external system accepted a request, sending the same action again may create a duplicate. Use stable request identifiers or read back the destination state before retrying.

Set limits on time, cost, tool calls, and repeated failures. An agent that cannot reach a safe conclusion should stop with a concise explanation and the context a person needs to continue.

Evaluate decisions, not just final prose

A polished answer can hide a broken process. Evaluation should cover the decisions and actions that produced the result.

Build a small set of representative cases before launch. Include normal requests, missing information, conflicting instructions, tool failures, permission boundaries, and requests that must be refused or escalated.

For each case, record the expected behavior. The expected result may be a completed action, a draft for approval, a request for missing information, or a safe refusal. Test the route the agent took as well as the final text.

Keep failed production cases, remove sensitive details, and add them to the evaluation set. This turns real mistakes into repeatable checks for later changes.

Log enough to reconstruct a run

You need to answer four questions after any important run:

• What was the agent asked to do?
• Which sources and tools did it use?
• What decisions and approvals changed the run?
• What action reached the external system?

Capture structured events rather than relying on raw model transcripts alone. Useful events include run start and finish, tool name, input references, outcome, latency, retry, policy decision, approval, and external action identifier.

Protect the log itself. Redact credentials and sensitive fields, restrict access, and set a retention period that fits the data involved. Observability should not create a second uncontrolled copy of user information.

Monitoring also needs an owner. Define which failures create an alert, where the alert goes, and who is expected to act on it.

Keep a person in the right loop

Human review works when it is reserved for decisions that need judgment or authority. If every minor step needs approval, reviewers start treating the queue as routine. If nothing needs approval, the system may cross a boundary before anyone sees it.

Use review for actions with material external impact, ambiguous intent, weak evidence, or sensitive data. Give the reviewer the proposed action, source evidence, policy result, and a clear choice to approve, reject, or edit.

Escalation should preserve progress. A person should not have to reconstruct the task from scratch because the agent encountered one uncertain field.

Plan operations before launch

Someone must own the agent after the first release. Decide who can pause it, change permissions, update prompts or tools, review incidents, and roll back a release.

Version the parts that affect behavior. A production record should identify the model configuration, instructions, tool versions, policy version, and evaluation set used for that release. When behavior changes, run the evaluation set again and record the result before expanding access.

Start with a small operating scope. Limit the accounts, actions, or queues the agent can touch. Expansion should follow evidence from completed runs and reviewed failures, not confidence from a demo.

A practical production-readiness checklist

Before launch, confirm that:

• the agent has one bounded job and a named owner;
• every tool has a narrow contract and enforced permissions;
• sensitive or external actions have an approval boundary;
• run state and terminal statuses are explicit;
• retries cannot silently duplicate side effects;
• time, cost, and tool-call limits are set;
• evaluation includes failures and escalation cases;
• logs can reconstruct important actions without exposing secrets;
• alerts have an owner and an expected response;
• the system can be paused and rolled back;
• post-launch cases feed back into evaluation.

A production-ready AI agent is not an autonomous demo with more prompts. It is a controlled product with a defined job, limited authority, observable behavior, and an honest way to stop.

Build for production from the first scope

Daia lists AI Agents & Copilots among its supported capability categories. Its reviewed engagement model states that work is scoped and priced before it starts, while timing, access, ownership, delivery, and handover terms are agreed for each engagement.

If you are planning an agent for a real workflow, start with the job boundary and the actions it may take. Then define the production and handover terms around that system. Get a quote to discuss the scope.