AI Agent Development Process

An AI agent development process should turn a promising task into a controlled business capability. The useful output is not a persuasive demonstration. It is a system whose permitted work, evidence, exceptions, operating owner and release conditions are understood. For an operations director or digital delivery lead, the process is therefore a sequence of decisions backed by increasingly realistic evidence.

Consider an illustrative freight company that wants help preparing evidence packets for damaged consignments. Staff currently reconcile delivery notes, photographs and correspondence before a claims specialist reviews the file. An agent might locate missing documents and prepare a referenced chronology. Deciding liability, agreeing compensation and contacting external parties remain separate permissions. This example follows the development process without assuming that Open for AI has deployed this particular system.

Begin with the work and its present failure modes

Observe a complete case from receipt to closure, including the waiting, corrections and handoffs. Interview the person who performs the task and the person who inherits its mistakes. A manager may describe the problem as slow document preparation, while the specialist identifies missing provenance as the real reason files return for rework. Those problems require different solutions.

Collect a permitted sample that includes successful, incomplete, disputed and out-of-scope cases. Record which information was available at the moment of each decision. A completed file can conceal the uncertainty faced earlier; using its eventual answer as an input creates an unrealistic demonstration. Redact unnecessary personal or commercial information and preserve the relationships needed to understand the task.

The discovery output should name the current unit of work, current handling effort, recurring failure categories, decision owner and downstream consequence. Describe uncertainty honestly when baseline measurements do not exist. The project scoping guide explains how to draw a defensible boundary around that work.

Choose the simplest credible operating design

Separate steps whose rules are stable from steps that require interpreting variable language or selecting evidence. In the freight example, checking whether a required file exists is ordinary software. Understanding that a photograph refers to a differently labelled consignment may need interpretation. Posting an approved status should still use an explicit application command with validation.

Anthropic distinguishes predefined workflows from agents that dynamically select their course of action. That distinction helps frame the design question: where does model-directed choice add value? It does not establish that more autonomy is better. See Anthropic's agent design discussion.

Write a short design hypothesis: “Given a consignment identifier and authorised evidence access, the assistant can assemble a source-linked chronology and identify missing evidence; it cannot determine liability.” Compare it with a form, search interface or deterministic workflow. If the simpler option solves the actual bottleneck, the discovery stage has succeeded even without an agent.

Define acceptance before improving prompts

Agree what a correct packet contains and what must never happen. For this example, every factual event needs a document reference, unsupported dates must remain unknown, and documents from another customer must never appear. A well-written narrative cannot compensate for a cross-customer disclosure or an invented delivery event.

Use a separate measure for usefulness: can the specialist review the packet with less reconstruction, and how much correction does it require? Keep correctness, handling effort, turnaround and escalation distinct. A fast result that creates additional review work may be commercially worse. Establish release thresholds from the consequence of failure and baseline evidence, rather than borrowing a generic accuracy percentage.

Store cases and expected behaviours in a versioned evaluation set. Include Arabic and English material where the intended workflow uses both. Reserve cases that the team does not repeatedly inspect while tuning. Detailed scoring and repeat-run methods belong in agent testing and evaluation.

Build a narrow vertical slice

A vertical slice connects one realistic input to one reviewable output through the intended access boundary. It should prove document retrieval, evidence references, structured output, an exception path and enough tracing to explain a failure. A beautiful interface around copied documents proves very little about production access or data quality.

Keep the first slice small enough to diagnose. Use one evidence repository and one packet type before adding every branch. Record assumptions explicitly: document naming quality, availability of a consignment key, supported image quality and expected document volume. Each assumption needs either evidence or a test, because an unnoticed assumption can become a late integration problem.

At this stage, a failed experiment is useful if it identifies the limit. For instance, unreadable delivery notes may show that the workflow needs a human transcription step. Repeated prompt changes cannot recover information absent from the source. The architecture guide covers how to separate retrieval, state, model decisions and controlled actions.

Turn the slice into a dependable service

Replace demonstration credentials with the intended identity model. Check access on every retrieval and action, not just at login. Define how the system identifies a case, retries a failed call, handles duplicate events and resumes after interruption. A draft saved twice may be inconvenient; an external action repeated twice can be consequential.

Design the review screen with the people who will use it. Show evidence next to consequential statements, distinguish missing information from an actual negative finding, and make correction and escalation straightforward. Approval should apply to a specific version of the packet. If its evidence changes afterwards, the system should not quietly reuse an earlier approval.

Prepare operating documentation while the design is still fresh. It should explain supported work, known limits, how to stop processing, who owns source updates, how to investigate a disputed output and what users do during an outage. Support ownership is part of delivery, even when ongoing support will be purchased separately.

A release evidence ledger for the freight example

The following is a proposed delivery artifact, not a report of completed testing. Its purpose is to connect each investment decision to evidence and an accountable reviewer.

Proposed evidence ledger for a freight evidence assistant

Decision point	Evidence required	Reason to pause
Proceed from discovery	Observed task, permitted examples and named claims owner	No reliable consignment-to-document relationship
Proceed from prototype	Referenced packet plus explicit missing-evidence behaviour	Fluent narratives hide unsupported events
Enter a supervised pilot	Access tests, recovery checks, review interface and operating owner	Cross-customer retrieval or uncertain write outcomes
Expand use	Reviewed pilot results by case type and language	Benefits depend on excluding the difficult cases
Accept into operation	Runbook, change process, support responsibilities and rollback route	No one can maintain source freshness

A decision ledger should also record what was deliberately excluded. Otherwise, a successful pilot may be interpreted as proof that disputed liability, external correspondence and financial settlement are already safe to automate. Expansion should create a new evidence requirement rather than inherit permission from the initial release.

Run the pilot inside a measurable boundary

A pilot needs a defined population, period, reviewer and exit decision. Initially, the agent can prepare packets while specialists retain their normal responsibility. Compare like-for-like cases and capture corrections, including the time needed to find and fix them. Count abandoned and escalated cases in the denominator; otherwise the easiest cases can make performance look artificially strong.

Separate learning from release assessment. The team may inspect a failed pilot case, improve the system and add a related regression test. That corrected example does not then become independent proof of general performance. Reassess with held-out or newly collected cases before expanding scope.

For bilingual operations, inspect failure patterns separately. Aggregate success can hide an Arabic document retrieval problem behind a larger English workload. In the same way, averages can hide difficulty with poor scans, multiple consignments or unusual evidence sequences. The pilot report should explain where the system is useful and where people still do the work.

Make launch a change in responsibility

At launch, a named owner accepts the supported scope and operating obligations. Confirm who can change instructions, publish source material, update integrations and approve a model change. Record the deployed versions and the evaluation evidence associated with them. A later “small” prompt edit can alter tool selection or escalation, so changes require proportionate review.

Monitor business completion as well as technical availability. A service can remain online while producing unusable packets or silently increasing review effort. Create a route for operators to flag defects without copying sensitive documents into uncontrolled channels. The monitoring guide explains the distinction between telemetry and proof of task quality.

Finally, make planning uncertainty visible. Access preparation, integration owners and review availability can determine delivery pace more than model selection. Use the development timeline guide to turn the process into a dependency-aware schedule instead of a universal delivery promise.

Keep commercial acceptance separate from technical completion

Agree the acceptance reviewer and review window before handover. A supplier can demonstrate that a retrieval connector works, but only the business owner can judge whether the resulting packet fits the real decision. Record both views. If the service passes its technical checks but still creates excessive specialist rework, the team should investigate the workflow or narrow the supported population before treating delivery as commercially successful.

Define the handover package as a set of usable assets: deployed configuration, source ownership map, evaluation cases, access documentation, known limitations and a procedure for restoring the previous release. Decide which assets the customer receives and which remain managed services under the agreement. This prevents a dependency on an individual developer who alone understands how the prototype works.

Acceptance also needs a route for defects discovered later. Distinguish a failure within agreed behaviour from a request for additional behaviour, and identify who triages each. A clear distinction supports a productive working relationship without allowing unresolved defects to disappear into a future enhancement list.

Open for AI's AI agent development service can scope this work around the customer's actual systems and responsibilities. To discuss a delivery process for your workflow, bring a redacted completed case, an incomplete case and the person who owns acceptance. Those materials make the first design discussion concrete.