The demo worked. Production never did.
The pilot handled the five examples in the demo. On real tickets it looped, guessed, or stalled, and there was no plan for the cases it could not handle.
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AI agents
AI Agents for Business Operations
AI agents that finish real work, not demos.
We build narrowly scoped agents with defined tools, guardrails, evaluations, and human handoff, so they complete a job your team can measure instead of impressing a meeting.
Start with a 30-day assessment Agent run · #4812SCOPED AGENT
GoalResolve a duplicate-charge ticket
01Read ticket + order historyhelpdesk · orders
02Confirm duplicate chargepayments (read only)
03Check refund policyknowledge base
04Guardrail: $340 is over the $200 limitlimit enforced in code
05Hand off to billing with summaryhuman approves
4 tools allowedEvery step logged
The operating problem
Most AI agents are impressive for ten minutes and unusable on Monday.
An agent that can do anything is an agent nobody can trust with something. The ones that survive contact with real work have one job, a short list of tools, hard limits on each action, a test set built from your cases, and a clear moment where a person takes over.
30 daysFixed-scope assessment
How it works in practice
What an effective agent run looks like
Illustrative support-refund agent. Every step is logged and replayable.
Agent run log
GoalResolve ticket #4812: customer reports a duplicate charge
01
Read ticket and order historyTool: helpdesk, orders API (read only)
Done02
Confirm duplicate chargeTool: payments API (read only). Two charges, same order, 40 seconds apart
Done03
Check refund policyTool: policy knowledge base. Duplicate charges are refundable
Done04
Guardrail: refund limitAmount $340 exceeds the $200 auto-refund limit
Guardrail05
Hand off to a personSummary, evidence, and drafted reply sent to the billing queue
HumanEffective agents have
One job with a definition of done
A short, explicit list of tools
Limits on every action
An evaluation set from real cases
Handoff with context when unsure
Hype-only agents have
A general assistant that does everything
Full API access and a careful prompt
Success judged by a demo
No logs, no cost tracking
No plan for the cases it gets wrong
Is this the right first step?
Good fit when. Not yet when.
Good fit when
A repeatable job with clear inputs and a definition of done
The needed information lives in systems with an API
Historical cases exist to test against
Someone owns the outcome and reviews escalations
Not yet when
The goal is a general assistant for everyone
A wrong action would be costly and cannot be limited
There is no way to measure whether the job was done well
The 30-day assessment says whichSignals it is time
If these situations feel familiar, this is worth assessing.
Nobody can say what the agent is allowed to do
It has an API key with full access and a prompt that says be careful. Nobody has written down which actions it may take, up to what value, without asking.
There is no way to tell if it is getting worse
A model update or a prompt tweak changes behavior quietly. Without an evaluation set, the first signal is a customer or an auditor.
AI agents by the numbersIllustrative figures from representative engagements
1job per agentNarrow scope is what makes an agent dependable
4tools, each with a limitRead-only by default, write access only where the job needs it
200+real cases in the evaluation setIllustrative. Built from your own history before launch
100%of actions loggedEvery step is replayable for review and audit
Illustrative scenarioA subscription software company with a six-person support team
The agent now closes the routine billing tickets and hands over the rest with the homework done.
“Situation
Billing questions were a third of all tickets. Each needed the same lookups across the helpdesk, the order system, and the payment provider before anyone could reply. A chatbot pilot had been switched off after it invented a refund policy.
What changed
We scoped one agent to billing tickets only. It reads the ticket, looks up orders and charges with read-only tools, checks the written policy, and may issue refunds up to a fixed limit. Anything above the limit or outside policy goes to a person with a summary and a drafted reply.
Result
An illustrative example of a typical engagement, not a named client case study.Routine billing tickets resolved without a person
Escalations arrive with evidence attached
Evaluation set run on every prompt and model change
What we build
A working system—not an automation slide deck.
The engagement is sized around one useful operational outcome. Your team receives the implementation, operating context, and visibility needed to own it.
Agent scope and success measures
One job, its inputs, the definition of done, and the numbers that prove the agent is worth running.
Tools, guardrails, and handoff
The systems the agent may touch, the limits on each action, and exactly when a person takes over.
Evaluation suite and operations
A test set from your real cases, run on every change, with logs, cost tracking, and alerts.
Delivery process
Diagnose. Design. Build. Measure.
Each stage has a clear decision and output, so the project remains connected to the business problem.
One job, defined done
We pick a single job with clear inputs and a measurable result, and collect real historical cases to test against.
Tools, limits, and handoff
The agent gets only the tools the job needs, a limit on each action, and an explicit handoff with context when it is unsure.
Tested on every change
The evaluation set runs on every prompt, model, or tool change. Logs, cost, and success rate are visible to the owner.
Side by side
The same work, done two ways.
Every row is a step your team handles today. The right column is what the workflow does after the build, with people kept where judgment is needed.
StepHow it happens todayAfter automation
Scope
Does everything, in theory
One job with a definition of done
Access
Full API key and a careful prompt
Named tools, each with a limit
Uncertainty
Guesses or loops
Hands off with context attached
Quality
Judged by a demo
Evaluation set on every change
Operations
No logs, surprise bills
Logged, costed, and owned
Works with your stack
AI agents built around the tools you already run.
We connect to what is in place through APIs, exports, and databases. Nothing here requires a platform change, and tools not listed are usually reachable too.
CClaudeModels
OOpenAIModels
n8nn8n agent nodesOrchestration
LGLangGraph and custom codeOrchestration
MCPMCP and API toolsTool access
HDHelpdesk and CRMSystems of work
EvEvaluation harnessQuality
LogLogging and cost trackingOperations
+Your other toolsAssessed in the first call
Questions about ai agents
Answered before you book.
Three questions we hear most often about this service. The rest is answered in the assessment.
How is this different from a chatbot?+
A chatbot talks. An agent completes a job by using tools: it looks things up, takes limited actions, and records what it did. We build agents only where there is a job to finish.
How do you stop it doing something it should not?+
It only has the tools the job needs, each action has a written limit, and anything beyond the limit goes to a person. Limits are enforced in code, not in the prompt.
Which model do you use?+
The one that passes your evaluation set at a sensible cost. The agent is built so the model can be swapped and re-tested without a rewrite.
What good looks like
Less handling. Fewer errors. Faster answers.
The agent completes a defined job end to end on real cases.
Every action is within a written limit and leaves a log.
Uncertain cases reach a person with the context attached.
Quality is measured continuously, not assumed.
How this engagement starts30day assessment
Thirty days inside the process and the tools around it. You finish knowing what to automate, with what, in which order, and how long it will take.
Start a 30-day assessment Fixed scope · read-only access · written findings you keepA 30‑day assessment before anything gets built.
Where your process stands
How the work really flows today: volumes, handoffs, waits, error rates, and the automations that already exist.
What you can do about it
Which steps to automate, which need an AI step, which should stay human, and which to leave alone.
Which tools will help
n8n, Zapier, Make, agents, or custom code: what fits your stack, team, and budget, and what would be hype.
What to improve in the process
Rules, ownership, and data fixes that should come before or alongside any automation.
How to work with legacy systems
If older software is in the way: how to connect to it, wrap it, or upgrade it so automation is possible.
How long it takes, and the roadmap
A sequenced build plan with durations, costs to expect, and the measures that prove it worked.
Week by week
What happens in each of the four weeks.
Kickoff and scope
We walk you through our in-house analysis system, which scans your code, repositories, and databases, and agree exactly what the assessment will cover and what you will receive.
- Scope, systems, and people agreed
- What you will get: architecture, workflows, recommendations, timeline, risks
- A go or no-go decision before any access is given
NDA, access, and the scan
Once you are ready to proceed, we sign an NDA and you grant read-only access to the necessary repositories and databases. Then our system runs.
- NDA signed, read-only access granted
- Data lineage traced across code and databases
- Workflows and dependencies identified
Verify and weigh the options
Senior engineers verify what the system found in working sessions with your team, and capture what is not written down anywhere.
- Findings confirmed or corrected with your people
- Options, tools, and process fixes compared
- Legacy upgrade paths tested against your constraints
Roadmap and readout
You receive the written findings and we walk the decision makers through them.
- Overall architecture and workflow documentation
- Recommendations and risks, in priority order
- Timeline and roadmap for the work
Who is behind AICO Services
Built and delivered by Trobus Technologies.
A Maryland-based engineering company. AICO Services is how we package our AI automation, modernization, and assessment work. Our engineers have delivered for organizations including these.
Organizations represented in the broader Trobus Technologies delivery history. Engagement scope and team role vary; reference details are shared where authorized.
Delivery recordRepresentative production engineering record. Details available where authorized.
4systems in production4 yrscontinuous operation990+automated tests1,361commitsCompany qualifications
Credentials maintained by Trobus Technologies, LLC
MBE
Minority Business Enterprise
MDOT
Maryland DOT certified
SBA WOSB
Woman-Owned Small Business
WOSB
Women-Owned Small Business
E-Verify
Participating employer
Free PDF · 2 pages
Get the 30‑day assessment specification.
The exact deliverables, the week-by-week schedule, where it applies, and the engagement terms. Share it with the people who need to approve the work.
- Six written deliverables
- Week-by-week schedule
- Engagement terms
A promise before the proposal
We will tell you when not to automate it. The assessment says so in writing.
30-day assessment
Start a 30-day assessment What could ai agents change for your team?
The 30-day assessment tells you whether it is a good candidate, which tools fit, and how long the build will take.