Create an AI Agent: Method, Guardrails and Acceptance Criteria

If you are looking to create an AI agent that is useful in production — and not just an assistant that answers — start by aligning method, architecture and guardrails. What follows is the order in which decisions are taken: what is written before a tool is chosen, how far the agent is allowed to act, which building blocks are assembled, and how you can tell it is ready to execute. For the general frame — what an agent is, where it creates value, what has to be locked down — the overview of AI agents sets out those markers.

What has to be written before you build an AI agent

An agent only creates value if it executes in your environment — data, tools, processes — and if you can prove what it has done. Before coding or connecting a single API, define a measurable steering frame: an agent in a company has to fit into the processes, respect guardrails and follow indicators, with complete traceability of actions. Without that, you are “automating” without making the autonomy acceptable. The overall signal is rather encouraging — 98% of companies using agentic AI report a return on investment (Squid Impact, 2025) — but a proportion is not a promise: it says the result is attainable, not that it comes without framing. These benchmarks and their variants are gathered in our review of GEO and generative AI statistics.

Set these prerequisites down on one page. It is the document you will reread in six months, when you have to decide between what the agent is allowed to do and what it is being asked to do.

• Objective: an observable result (cut the time spent triaging technical anomalies, speed up updates to high-impact pages).
• Data: permitted sources, update frequency, minimum quality.
• Access rights: read-only or write, separate environments (dev/staging/prod), least privilege.
• Success criteria: before/after indicators (turnaround, error rate, volume of recommendations accepted).

Two decisions are taken at that moment, and no later. The first: the agent reads everything, but writes little, and only within a controlled frame. The second: its rights on your publishing tools stay limited to drafts, never to direct publication, for as long as the measurement loop has not been validated. These two rules cost very little to set at the start, and become almost impossible to impose after a first successful deployment.

Choosing the level of agency, and where the agent stops

Most failures come from a badly chosen “level of agency”: too autonomous too early, or not tooled enough to act. Operationally, an agent perceives a context, reasons according to an objective and acts through tools — API calls, files, tickets — with or without a human in the loop; what separates it from an assistant is the orchestration of traceable actions, not the quality of the text produced. The right level depends on three factors: risk (brand, compliance), complexity (number of steps) and access to tools. You save time by reducing the ambition first, then industrializing.

Four levels, from diagnosis to bounded autonomy

Autonomy is not binary: it is set, and it is set in writing. Each level commits you to a different control set-up, and it is that set-up — not the capability of the model — that decides what you can actually deploy.

Moving from one level to the next is not decided on an impression, but on what the execution logs of the previous level show. An agent that has held three weeks under supervision without triggering an automatic stop has demonstrated something; an agent people find “pretty good” has demonstrated nothing at all.

Stop thresholds, and why the chain has to stay short

Reliability does not degrade in proportion to the number of steps: it collapses as the chain lengthens, because each step inherits the approximations of the one before. The design consequence is simple: cut the number of steps, and move the checks out into code and rules rather than entrusting them to the model. Your design therefore has to say where the agent stops — insufficient evidence, inconsistency between two sources, irreversible action — and what happens then: a clean stop, then a handover to a human with the context, never a blind retry. An agent that does not know how to stop is not an autonomous agent; it is an agent nobody is watching.

The four building blocks of an AI agent: model, tools, memory, orchestration

Building a robust agent comes down to separating “thinking” from “acting” clearly, then instrumenting each step. The minimum architecture includes a model, a toolbox, a memory and an orchestration layer. The model is there to interpret, plan and produce structured outputs — not to guess your business rules. Those are written elsewhere, in code and thresholds, where they stay readable, testable and changeable without touching the model. It is that split that makes the agent’s behaviour reproducible.

The toolbox and the memory: what the agent reads, what it keeps

An agent only executes if it can call tools: read data, prepare a draft, create a ticket. Each tool must expose limited, documented functions controlled by permissions. Avoid “admin access everywhere”: it is an anti-pattern for security as much as for governance. Prefer a catalogue of narrow, auditable functions.

• Data connectors: extraction of measurements, inventory of pages, editorial templates.
• Action functions: create a ticket, propose a fix, create a draft, request an approval.
• Validators: output schemas, consistency rules, risk thresholds.

On the memory side, distinguish two needs. Short-term memory holds the thread of the task in progress. Persistent memory builds things up — decisions, exceptions, preferences, histories — but it raises the compliance and security stakes, and is therefore not decided on for convenience. When the agent has to answer or decide from internal documents, the answer is controlled grounding on a corpus: indexing, the retrieval strategy and the citing of sources are the subject of the RAG AI agent.

The plan → action → observation → decision loop

A robust agentic loop looks like a control system: it observes, decides, acts, then observes again. That avoids the fragile “one shot” mode. Separate two loops: a “plan” loop that proposes a list of actions, and an “execution” loop that applies one action at a time. The plan produces a structured format, with a rationale and a piece of evidence; the execution checks the preconditions, then logs the effect.

• Plan: select a candidate priority action and produce a strictly structured output.
• Action: execute through a connector, with idempotency.
• Observation: collect the metrics and the state, before and after.
• Decision: carry on, escalate to a human, or stop (threshold reached, risk).

This separation is a design decision, not an implementation detail: it reduces drift and makes supervision possible. If you write this loop yourself, its project structure, its patterns and its tests belong to the Python AI agent. And if your problem is no longer one agent but several — division of roles, exchange protocols, conflict arbitration — then AI agent orchestration takes over: here, one agent and its loop.

Writing the specification and the decision protocol

The quality of an agent depends less on the model than on your specifications. An agent that does not know what to produce (format), when to stop (thresholds) and how to prove (logs) becomes unpredictable. In B2B, traceability is a prerequisite for acceptance, not a bonus: every action has to be recorded and auditable. None of this requires writing code: the same specification holds in a visual builder, where it is worth exactly as much. It is the content of the document that protects you, not the language it ends up running in.

The specification, a contract between the model, your tools and your team

An agent specification is a contract: it sets the interface between the LLM, your tools and your team. It reduces ambiguity, and therefore hallucinations and drift, and it makes automated tests possible. Write it before automating anything at all.

A specification that fits on one page is worth more than an exhaustive document nobody will reread. The test is simple: a colleague who does not know the project should be able to say, by reading it, whether a given output complies or not.

The decision protocol: scoring, business rules and guardrails

A useful agent does not “recommend” at random: it arbitrates. Your protocol has to make the decision explainable and stable, even when the model varies. Use multi-criteria scoring, then apply filtering rules. That is how you reduce the biases — overrating visible but unprofitable actions, for instance.

• Scoring: estimated impact, effort, risk, dependencies, urgency.
• Business rules: prohibitions (legal pages), thresholds (minimum traffic), windows (release freeze).
• Guardrails: stop if the evidence is insufficient, if the data is inconsistent, or if the action is irreversible.

Written this way, the protocol becomes contestable: a decision can be replayed, discussed, then corrected by changing a rule — not by rewriting a prompt and hoping the behaviour changes.

Handling error and proving what was done

Errors are not “exceptions”: they are normal — interfaces unavailable, quotas reached, incomplete content, unexpected formats. A robust architecture formalizes recovery instead of blindly repeating the same call, avoids double writes and plans a rollback, at least a logical one, as soon as a step changes a system.

• Timeouts: cut cleanly, then escalate.
• Retries: retry only if the error is transient, and a limited number of times.
• Idempotency: a unique operation identifier, to avoid duplicates.
• Rollback: undo, restore, or create an automatic correction ticket.
• Handover: transfer to a human with the context, the logs and a proposed resolution.

Logging the decision, not only the action

Without logs, you can neither debug nor convince. Log not only the action, but also the decision and its evidence. And version prompts and rules, because an agent evolves continuously: without versioning, you will not be able to say what changed between a correct run and a failed one.

• Run identifier, timestamp, input and output, model used.
• Decision: scores, rules applied, thresholds triggered.
• Evidence: state before and after, the original measurement, the page concerned.
• Versioning: prompts, schemas, connectors, rules, configuration.

These four objects serve two audiences that are often confused. The first two serve whoever has to fix the agent; the last two serve whoever has to authorize a wider scope. A set-up that produces only technical logs will never get past the committee stage.

Classifying the data, and closing what the agent has no business opening

66% of users rely on AI outputs without checking their accuracy (Squid Impact, 2025). Verification is therefore not an engineer’s precaution: it is precisely what nobody does spontaneously, and that is why it has to be carried by the system rather than by goodwill. Classify before integrating, and impose automatic filtering at the input.

• Acceptable with conditions: aggregated metrics, public URLs, non-sensitive extracts.
• Forbidden: personal data, secrets, credentials, internal documents that have not been anonymized.
• Cases to approve: detailed logs, content before publication, customer data.

Secrets are stored outside the code, separated by environment and renewed regularly. That leaves the risk of malicious instructions: an agent can be manipulated by booby-trapped content it retrieved itself. Protect it through action policies — a whitelist of functions, strict validation of inputs, separation of plan and execution — log every attempt to step outside the scope, and add an automatic stop threshold in case of uncertainty.

Acceptance testing before letting go, then monitoring

56% of users say they have made mistakes because of AI (Squid Impact, 2025). A low error rate is not a zero rate, and it is the test set that reveals it — never the demonstration, which by construction shows only the nominal path. So test like software, not like a demo: the question is not “does it work”, but “what happens when it does not”.

Test sets, acceptance criteria and non-regression

Acceptance testing for an agent is prepared like that of a software component, in three parts. They are gathered in a single document, reread at every change of prompt, rule or connector.

• Test sets: “normal” pages, sensitive pages, missing data, quotas reached.
• Acceptance criteria: rate of valid outputs, escalation rate, rate of actions correctly refused.
• Non-regression: same input → same decision, within an acceptable range.

The third criterion is the one people forget, and it is the most discriminating: an agent whose decision varies from one run to the next on an identical input is not ready, whatever the average quality of its outputs. Check as well that a forbidden action really is blocked: a correct refusal is a test result just as much as a success.

What you monitor once in production

Without metrics, steering is impossible. Monitor three families of indicators, and draw a clear line between inference cost and operational cost — maintenance, incidents, human approval — because it is the second that decides an agent’s real profitability.

• Agent metrics: success rate per step, latency, escalation rate to a human.
• Business metrics: volume handled, backlog cleared, effect measured over a consistent period.
• Costs: model calls, API calls, storage, supervision.

An agent ages fast: interfaces change, rules evolve, templates move. Plan for maintenance as a recurring load, not as an incident. And roll out progressively with canaries — a small scope first — then widen; always keep a rollback plan, including when everything is going well.

A worked example: from detection to an executable backlog

The hard part is not “detecting”: it is prioritizing without bias and connecting the decision to a real stake. Start from a usable map of signals, not from a list of ideas. For each signal, require measurable evidence, then connect it to a family of actions: fix, optimize, create, consolidate. Pages with zero impressions call for a technical diagnosis and a ticket; a drop in clicks with stable impressions calls for work on intent and on the hook; deep pages with potential call for an internal linking plan. A signal without evidence remains an intuition, and an intuition should never enter a backlog.

Prioritization then has to survive reality: limited time, multiple teams, brand constraints. Your scoring has to be simple, replicable and contestable — estimated impact, effort, risk, dependencies — and refuse “magic” scores with no justification. The risk factor is not decorative: it is what protects critical pages from an optimization that is technically correct but commercially absurd.

Finally, a prioritized list is not enough: the order of execution counts. Sequence by dependencies and by quick wins at low risk, so as to validate the measurement loop before widening the reach. That is how a step up in autonomy is played out in acts rather than in principles.

• Batch 1: low-risk technical fixes, with metrics that are easy to follow.
• Batch 2: content optimizations with systematic human approval.
• Batch 3: production of drafts and controlled publication on non-critical scopes.

FAQ: frequently asked questions about creating an AI agent

What is an AI agent and what is it for?

An AI agent is a software system that perceives a context (data, signals), reasons according to an objective, then acts through tools — API calls, drafts, tickets — with a bounded level of autonomy. It serves to run multi-step tasks more autonomously than a simple assistant, while remaining measurable and auditable. Its value does not come from the model: it comes from what you have specified, authorized and logged around it.

Which types of AI agent can be created, depending on the use?

Four families cover most needs in marketing and content. An audit agent detects anomalies, consolidates the evidence and creates the tickets. A prioritization agent scores impact, effort, risk and dependencies. A production agent prepares briefs, drafts and variants, but rarely pushes anything without approval. An execution agent applies targeted changes, with a rollback. The right type depends on your risk and on your ability to instrument execution.

Is it possible to create your own AI?

Yes, in the sense that you can design your own agent by assembling a language model, an orchestration layer and tools, with your data and your rules. Training a foundation model from scratch, on the other hand, is a very different project, expensive and rarely relevant for a marketing team. The realistic route is to build an agent around an existing model, adding document grounding and guardrails. Learning is iterative: expect several cycles, not a few days.

How do you create a simple AI agent?

Start with a non-critical task and a very short chain, because reliability degrades as steps pile up. Define one input, one structured output and a single tooled action — creating a ticket, for instance. Add a human in the loop to approve every output. Then measure the time saved and the errors before adding a single extra step.

Which steps should you follow to create an AI agent end to end?

In order: write the steering prerequisites (observable objective, permitted data, rights, success criteria), choose the level of agency and its stop thresholds, assemble the building blocks (model, tools, memory, orchestration), write the specification and the decision protocol, handle error and logging, run acceptance testing on test sets, then deploy on small scopes with a rollback plan. The stage most often skipped is the sixth, and it is the one that separates a prototype from an agent in service.

How do you design robust tool orchestration and error handling for an AI agent?

Separate planning from execution: one loop that proposes actions, another that applies one at a time. Then treat errors as a normal flow — timeouts, limited retries, idempotency, rollback and handover to a human — rather than as exceptional cases. Add stop thresholds and complete logging of decisions, not only of actions. Finally, test under varied scenarios, all the more so as the number of steps grows.

How do you create an AI agent with GSC, GA4 and CMS integrations?

Start read-only, long enough to check that the data consumed is stable and that the diagnosis holds. Then move to writing under approval, limiting the agent to creating drafts and queuing them for approval. Use dedicated service accounts, minimal roles and isolated environments. Success turns on traceability: for every action, keep the evidence and the state before and after.

How do you create an AI agent that automatically prioritizes SEO work by business impact?

Use a multi-criteria decision protocol: estimated impact, business value, effort, risk and dependencies. Require measurable evidence for each signal and refuse any recommendation without a justification. Then sequence the actions so as to validate the measurement loop on quick wins before tackling the heavy work. What you get is an actionable, contestable backlog, not a theoretical list nobody dares execute.

What does an AI agent cost?

The cost breaks down into items, and it is that breakdown that matters more than any amount: model calls, calls to third-party interfaces, storage, human supervision, maintenance of the connectors and the rules. The most underestimated item is the last: an agent ages with every change of API or template. The right approach is to start from a narrow scope, measure the gain, then widen only once the operational cost is known.

Can you create an AI agent locally without exposing your data?

Yes, it is possible, but “local” does not mean “risk-free”. Running locally reduces data exposure, provided access is secured and the hosting chosen is compliant, particularly for sensitive content. It does, however, make maintenance more complex: models, dependencies, performance, reproducibility. You also have to keep the same policy of minimizing the data sent to the model, and the same logging.

How do you get data security and API secret management right in an AI agent?

Apply three principles: data minimization, least privilege and auditability. Store secrets outside the code, segment them by environment, and plan scheduled rotation and fast revocation in the event of an incident. Audit access — who, when, from where — and alert on abnormal use. Finally, add guardrails against injections and unauthorized actions, through a whitelist of functions and strict validation of inputs.

Which architecture should you choose between persistent memory and “stateless” execution?

Choose “stateless” if your tasks are short, highly testable and you want to reduce data exposure. Choose persistent memory if you need personalization, history and operational learning — but you will have to strengthen security and compliance. A frequent compromise is to keep a persistent “business” memory (decisions, tickets, rules) and to limit sensitive conversational memory. In every case, version and audit.

How do you assess an agent’s reliability before letting it execute autonomously?

Test on multiple scenarios, measure error rates per step, and impose a human in the loop at the start. Check three things: that the output format is respected, that the evidence is present, and that a forbidden action really is blocked. Add stop thresholds and a rollback policy. Then widen the autonomy only on low-risk, highly repetitive actions, relying on the logs rather than on an impression.

Continue reading

• Your specification holds, but the sequence of steps, the branching and the recovery still have to be drawn: that is the subject of the AI workflow agent.
• You have to connect the agent to your real tools and settle the write rights: connectors, service accounts, quotas and environments are covered by AI agent integration.
• You want to validate the method without mobilizing a technical team: the full approach, from first test to scaling up, is that of the no-code AI agent.
• Your context calls for auditability or controlled hosting: licences, sovereignty and operational load are compared on the open source AI agent.
• Your teams already work in the editor and want the agent to act there: what it does on an open repository and what it must be forbidden belong to the VS Code AI agent.
• You have to judge an existing agent project or trigger it from your continuous integration chain: that is the ground of the GitHub AI agent.
• The method is settled and what remains is choosing what to build with: models, vendors and automation tools are compared on an AI agent platform.

And if the point blocking you is the first bullet of your prerequisites — having permitted, up-to-date and consistent data sources before connecting anything to them — an SEO and GEO steering platform handles that precise task.