AI agent development services for systems that act, not systems that demo.
An agent decides what to do next. That is the whole value and the whole risk. Most agent projects fail on the same four things: no autonomy boundary, over-scoped tool permissions, no step budget, and no baseline to prove it worked.
I design and build agents that know what they may do, escalate when unsure, log every decision, and go live one decision class at a time.
SculptAI is a multi-agent system that raised $350K in seed funding; solving inter-agent context drift there took it from demo to a 70% reduction in development time. A separate 20-agent trading intelligence ensemble runs where the domains are genuinely independent.
Do you need an agent, or a workflow?
This is the first question, because getting it wrong is the most expensive mistake available. An agent is a system where the model decides what to do next. A workflow is a system where you decided, and the model does one step well.
If you can draw the process as a flowchart and its shape does not change from case to case, you want a workflow with model-powered steps - cheaper to run, dramatically easier to debug, and explainable to an auditor by pointing at the diagram. That is human-approved automation, not autonomy. You need an agent when the next step depends on what the system discovers partway through. I will tell you which one you are looking at on the first call, including when the answer costs me the bigger project.
What gets built
Note where the effort sits. It is almost never where buyers expect:
Autonomy boundaries
An explicit list of what the agent may do without asking, what it must propose for approval, and what it must never touch. A business decision documented by an engineer - and it belongs in the contract.
Tools with scoped permissions
Every capability is a tool with a scope. An agent that reads your whole CRM to answer one question about one account is a data-protection problem wearing a productivity costume.
Orchestration and state
How the agent tracks what it has done, recovers when a tool fails, and stops. Unbounded retries are the most common way an agent turns a small failure into a large invoice.
Per-step observability
Reasoning, tool calls, inputs and outputs logged as they happen. Without it you cannot debug, cannot improve, and cannot answer a regulator asking how a decision was reached.
How the rollout runs
- Baseline. Measure the current manual process - volume, handling time, error rate, escalation rate. Without a number to beat, “the agent works” is an opinion.
- Autonomy boundary. Decide and document what the agent may do unsupervised. In regulated work this runs alongside the impact assessment rather than after it.
- Tools first. Each tool built and tested independently, with its permission scope, before any agent uses them. These integrations keep their value even if the agent is cancelled.
- Shadow mode. The agent runs on live cases and proposes actions; humans act. Nothing it decides takes effect. This is where you find out it is right 80% of the time and confidently wrong in a category nobody anticipated - without a customer on the other end. I would refuse to skip it.
- Graduated release. Autonomy extended one decision class at a time, each with a rollback switch, so the blast radius is never larger than one class.
How you know it is working
- Agreement rate in shadow mode - how often the proposed action matches what the human did, on real cases. Your go/no-go number.
- Escalation precision - when the agent says it is unsure, is it right to be? Good judgement about its own limits earns more autonomy than raw accuracy.
- Cost per completed task - measured including retries, not estimated. Decides whether it scales past the pilot.
- Time to resolution against the baseline - the one that survives a budget review.
Where the agent needs to be right, not just fast
When decisions rest on retrieved documents, citation discipline is what makes human review possible at all. My published evaluation runs - including 0 hallucinated citations across 297 cases against public benchmark suites - are on the proof page with the methodology. If your agent reasons over a document corpus, pair this with RAG implementation.
Who this is for
- Operations teams drowning in unstructured inbound where the right next action varies case by case.
- Regulated firms that need a defensible human decision point, not a rubber stamp on a queue of 300 items.
- Product teams adding agentic features that must not act unsupervised on customer data.
- Rescue work - an agent that loops, drifts, or costs more per task than the humans it replaced.
Start with one decision
Tell me the decision your team makes most often, and what happens today when it is made wrong.
I will tell you whether it is an agent, a workflow, or neither - and what the first two weeks would produce.
Book a Strategy CallWhere I work
Remote-first, with business-hours overlap and code in your own accounts and regions.
Frequently asked questions
What do AI agent development services include?
Five workstreams: autonomy design (what the agent may do unsupervised, what it must propose for approval, what it must never touch), tools and scoped permissions, orchestration and state management including loop termination, per-step observability so any decision can be reconstructed, and the human approval and escalation interfaces. The orchestration framework is the smallest part of the work; autonomy boundaries and tool permissions are the largest.
Do I need an AI agent or an automated workflow?
Apply the branching test. If the process fits a flowchart whose shape does not change from case to case, build a workflow with a model inside one step: cheaper to run, easier to debug, far easier to explain to an auditor. You need an agent when the next action genuinely depends on what the system finds partway through. Most companies asking for agents need a workflow.
How long does an AI agent build take?
Six to nine weeks for a single-purpose agent with two to four tools and a human approval step: one to two weeks defining the decision boundary and instrumenting the current manual process, three to four weeks building tools and orchestration, and two to three weeks on observability, failure handling and shadow-mode running. Multi-agent systems take longer because inter-agent context handling and failure isolation are where the hard engineering sits.
How do you stop an AI agent doing something expensive or irreversible?
Four mechanisms, all inexpensive: tool permissions scoped to the minimum, a step and cost budget per task, an approval gate on every irreversible action - sending, paying, deleting, publishing - and a kill switch that disables autonomy without taking the service down. Any proposal that does not mention the kill switch has not thought about failure.
How many agents should a system have?
Start with one and split only when you can name the reason. Decomposing into a dozen specialists usually multiplies coordination overhead without adding capability, and information degrades as it passes between agents. Large ensembles are justified when the domains are genuinely distinct and independently verifiable - the exception, not the template. The patterns are covered in the multi-agent production guide.