AI AGENT DEVELOPMENT SERVICES
I build agents for research, document processing and content operations. Each has a specific task, access to the right tools, and a clear point to ask a person for help.
Free introductory call.
A $1,500 diagnostic, credited toward a fixed-price build.
Use the brand’s inputs and relevant research.
Draft the copy and produce the visual assets.
Send images for review and use the feedback for revisions.
Hand the accepted campaign to the publishing tool.
START WITH THE TASK
The useful question is which part of your process needs flexible judgement, and how you will know the result is acceptable.
RESEARCH & CONTENT
Retrieve relevant material, structure a response and prepare it for your publishing process.
DOCUMENT OPERATIONS
Extract fields, classify documents and route the result into the tools your team already uses.
CHOOSE THE RIGHT APPROACH
An agent is useful when a task needs flexible judgement. Other work benefits from a simpler, more predictable approach.
Totals, eligibility rules and other defined calculations need reproducible logic. A language model should not be the authority for the answer.
Better fit: Use SQL, ordinary code or a rules-based workflow, with validation and review of exceptions.
A fixed sequence of actions gains little from an agent choosing the same path repeatedly.
Better fit: Use a scheduled or event-driven workflow with explicit branches, retries and error handling.
An agent cannot reliably supply facts that the business does not have or resolve unclear ownership of records.
Better fit: Clarify the process, improve the source data and add validation before automating decisions.
A consequential action should not happen just because a model proposes it.
Better fit: Use authenticated approval steps and constrained actions. Let the agent prepare the recommendation or supporting material.
If the team cannot describe an acceptable result, there is no sound basis for evaluating an autonomous system.
Better fit: Run a manual pilot, collect representative examples and agree acceptance criteria first.
A low-volume task may cost less to handle directly than to build and maintain an agent around it.
Better fit: Use a checklist, template or small utility, and revisit automation when the repeated work warrants it.
A SYSTEM YOU CAN INSPECT
This n8n case study connects research, platform-specific copy, generated images and scheduling. Images go to Slack for approval; rejected images are revised using the reviewer’s feedback. It shows where a person participates in a concrete workflow.
Read the full case studyA brand brief and the campaign requirements.
Research, platform-specific drafts and images.
Image approval and feedback in Slack.
Approved assets stored and scheduled through Late.
BEFORE IT GOES LIVE
An agent needs acceptance criteria, failure examples and an operating plan, alongside the prompt.
EVALUATION
Use representative inputs, known answers where available, and examples of incomplete or misleading inputs.
HANDOVER
Give the team a way to inspect decisions, review exceptions and adjust the workflow.
WORKING TOGETHER
The free call is an introduction. The diagnostic is the paid engagement that defines the build.
01 / DIAGNOSTIC
Identify the task, tools, data access and approval points. Agree the evaluation cases and a fixed-price implementation scope.
$1,500Credited toward the build02 / IMPLEMENTATION
Implementation, checks with representative data, deployment and documentation on accounts you control.
From $5,000Fixed price, agreed before implementation03 / ONGOING SUPPORT
Optional monitoring, fixes and improvements from the person who built the system. The support scope is agreed separately.
Scoped to your systemOptional after launchGO DEEPER
Explore the Claude workbook, or examine a document-processing workflow that extracts financial records and makes them queryable.
AI agents, explained
AI agent development services build software agents that carry out real, scoped tasks — research, document processing, content operations — using a perceive → plan → act → check loop, with real tools and human oversight where it matters.
A chatbot answers messages; an agent completes work. It plans multi-step tasks, calls real tools (APIs, files, databases), checks its own output against criteria, and escalates to a human when needed — running inside your workflows, not just a chat window.
Scope it to a defined task, give it only the tools it needs, evaluate outputs against real criteria, and add human approval at the points that carry risk — then monitor it like any production system.
A short discovery to define the task and success criteria, then the build (tools, prompts, evaluation), a review against real inputs, and deployment into your existing stack — with handover so you own and can run it.
Straight answers on cost, ownership and what happens after launch.
Bring a representative input and an example of the output your team needs.