AI Agent Implementation
Design and build of orchestrated multi-agent systems integrated with your core platforms — ERP, CRM, EHR, LOS — with human-in-the-loop controls and full auditability.
AI agent implementation is the engagement that takes one chosen workflow from design to a production system your team owns. The work runs through design, a staged build with acceptance gates agreed before building starts, and a handover of documentation, runbooks, and operator training. Agents handle the volume; people keep the decisions you choose to keep.
On this page
At a glance
- For teams that have already chosen a workflow and can name its volume, systems, and owner.
- Covers design, a staged build, and handover, with acceptance gates agreed before building starts.
- People keep the decisions you choose to keep; agents act only where they've met agreed thresholds.
- You own the implementation, with documentation, runbooks, and trained operators at handover.
Who this is for
Implementation is for organizations that have already chosen a workflow. Maybe it came out of a 360° AI Blueprint, maybe the business case was obvious from the start. Either way, you can name the process, roughly how much of it there is, which systems it touches, and who is accountable for its outcomes.
Four people usually need to be satisfied before a system like this goes live, and this page tries to answer each of them. The process owner will run it, and the executive sponsor is paying for it. The IT or security reviewer has to grant it access, and the procurement evaluator has to compare it with alternatives.
When an agent system is the wrong tool
An agent system earns its cost when the work combines volume, judgment on inconsistent inputs, and several systems that don't talk to each other. Without those, something simpler is usually better, and we'll say so.
| If this is true | Consider instead |
|---|---|
| The rules are fixed and the inputs are already structured | Deterministic workflow automation, or configuration in the ERP or CRM you already own |
| The volume is low or the work happens a few times a quarter | A checklist and manual review; automation will cost more to maintain than it saves |
| Your existing software has a module for this that you haven't turned on | Evaluate and configure that module first |
| The real problem is upstream: missing fields, unclear policy, no owner | Fix the form, the policy, or the ownership; automating a broken process makes it faster, not better |
| Decisions are high-stakes and irreversible, and nobody has time to review | Don't automate the decision; at most, prepare the evidence for a human |
| There is no way to measure the current process | Establish a baseline first, or you won't know whether the system worked |
Our guide to human-in-the-loop AI governance goes deeper on deciding which steps a person must keep.
What the engagement covers
Implementation runs through the Design and Implement stages of our delivery process and leads into Enable & Hand off, which is also offered on its own as Ownership & Enablement. The stage descriptions below are the ones we publish for the process as a whole.
| Stage | What happens |
|---|---|
| Design | System architecture, agent roles, guardrails, and integration plan — reviewed with your compliance and IT leadership. |
| Implement | Build and ship to production in controlled stages, integrated with your systems and with minimal disruption to operations. |
| Enable & Hand off | We optimize, train your operators, and hand over documentation and runbooks. Your team owns and runs the system — no vendor overhead. |
Before building starts, the written scope for a workflow typically names:
- the workflow boundary: where the system's work starts and stops, and what stays manual;
- each system it reads from or writes to, and the access it needs in each;
- every action the system may take on its own, and every action that needs a person's approval;
- the evaluation set and the acceptance thresholds each stage has to meet;
- what is handed over at the end, and who on your side receives it.
Typically outside an implementation's scope, unless the engagement agreement says otherwise:
- running the system day to day after handover, which is your team's job by design;
- model-provider, hosting, and third-party software fees, which are usually billed to you directly;
- legal, compliance, or security sign-off, which stays with your own counsel and reviewers;
- fixing upstream data quality or policy gaps the design uncovers, beyond flagging them.
We don't publish prices or standard timelines. Both depend on the integration surface, the state of the data, and how strict the acceptance thresholds are. Final scope, deliverables, and terms are set in the written engagement agreement.
A representative architecture
Every system is designed around its workflow, so there's no fixed product. The components below are representative of how we structure a production agent system; they're not a description of any specific client deployment.
| Component | Its job | What a reviewer should ask |
|---|---|---|
| Connectors | Read inputs from, and write results to, your existing systems through their APIs, exports, or file drops | What credentials does each connector hold, and can any of them write? |
| Orchestrator | Routes each case through the steps of the workflow and keeps its state | What happens to a case when a step fails or times out? |
| Specialized agents | Narrow roles such as extracting fields, classifying a case, or drafting a response, each with only the tools it needs | Can any agent take an action outside its role? |
| Deterministic rules | Checks that must always hold, applied in code rather than left to a model | Which checks are code and which rely on model judgment? |
| Review queue | Where people approve, correct, or reject what the system proposes | Who sees the queue, and what evidence comes with each item? |
| Audit log | A record of every input, model output, rule result, human decision, and action taken | Can we reconstruct why any single case ended the way it did? |
| Evaluation and monitoring | Test sets run before every release, plus live measures of quality, volume, and cost | What triggers an alert, and who receives it? |
Two design choices matter more than any model. First, anything that can be checked with a rule is checked with a rule; models handle what rules can't, such as reading an unstructured document.
Second, each agent's access follows least privilege: read-only wherever possible, narrow write permissions where not, and nothing that can move money or change a record of consequence without an approval step your process owner defines.
Acceptance gates
In our typical approach, a system reaches production through a sequence of gates. The thresholds are agreed with you before building starts and measured on your own cases, and the process owner signs off each gate. If a gate isn't met, the system doesn't advance.
- Design sign-off
Architecture, agent roles, guardrails, and the integration plan are reviewed with your compliance and IT leadership.
- Offline evaluation
The system runs on a held-out set of historical cases with known correct outcomes. Results are reported per field and per decision type, including where it abstains.
- Shadow run
The system runs alongside the current process without acting. Every disagreement between the system and your team is reviewed and explained.
- Supervised production
The system acts on live work, with people approving the actions the scope reserves for them. Error, override, and abstention rates are tracked against the agreed thresholds.
- Expanded autonomy, where earned
Only actions that have met their thresholds move to less supervision, one action type at a time, and each can be rolled back.
- Handover acceptance
Documentation, runbooks, and operator training are complete, and your team has run the system without us.
What we typically need from you
- A process owner with authority Someone who sets thresholds, approves gates, and decides which actions stay with people.
- Historical cases with known outcomes Enough to build an evaluation set that reflects the real mix of work, including the hard cases.
- Access through your IT team Granted per system and documented in the scope, starting read-only.
- Reviewer time During the shadow run and supervised production, when your team compares the system's work with its own.
- A baseline For the current process, or agreement to measure one before building, so the result can be judged.
Please don't send confidential client, patient, credit, or policy documents through the website. Data sharing is arranged directly once scope and handling have been agreed.
Security, access, and data handling
- Access is granted by your team, per connector, and documented in the scope. We start read-only and add write access only where the workflow requires it.
- Which model providers are used, and under which data-retention terms, is a design decision reviewed with your security team, not a default we assume.
- Sensitive data stays where your policy says it can go. If a workflow involves regulated data, the design is reviewed against your own requirements and counsel.
- This page makes no certification claims. If your procurement process needs specific attestations, raise it on the first call so it's addressed before scope is agreed.
Our list of questions to ask an AI consulting firm is a reasonable starting point for your own security and procurement review.
Ownership and handover
Our wording for this is plain: You own the implementation. We train your operators, hand over documentation and runbooks, and leave you a system your team runs without vendor overhead.
Handover typically includes the following, with the exact list set in the engagement agreement:
- architecture and configuration documentation, including every rule and threshold;
- runbooks for the failures you are likely to see, such as a source system changing format or a model provider being unavailable;
- the evaluation sets and the procedure for re-running them before any change;
- training for the operators who work the review queue and for whoever maintains the rules.
Costs to plan for after launch
A production agent system has running costs that a business case should include from the start. They include model usage, hosting, monitoring, and the time your reviewers spend in the queue. They also include maintenance when a policy, a vendor, or a source system changes. We model these with you during design so the return is judged net of them.
What you can verify
This page doesn't cite client results or outcome figures. For your own workflow, the evidence that matters is the acceptance gates: results on your own historical cases, against thresholds you set, reviewed by your process owner before the system acts on live work.
Where this applies
- Our use-case pages describe specific workflows in this level of detail. Each covers inputs, human decisions, failure modes, and acceptance criteria. Examples: academic scheduling optimization, insurance invoice analysis, financial reconciliation automation, and document review with human approval.
- If your workflow runs on older systems without modern APIs, automating legacy systems without migration explains the options.
- For our longer take on how implementation projects are structured, read our article on AI agent implementation.
Common questions
Do we own the system you build?
Yes. You own the implementation. We train your operators, hand over documentation and runbooks, and leave you a system your team runs without vendor overhead.
How long does an implementation take?
It depends on the number of systems involved, the state of the data, and how strict the acceptance thresholds are. We don't quote a standard timeline; the written scope sets one for your workflow.
Will the system make decisions on its own?
Only the ones you decide it may make, and only after it has met the agreed thresholds for that type of action. Everything else goes to a person with the evidence attached.
What if the evaluation shows the system isn't good enough?
Then it doesn't advance. We'll show you where it falls short and whether a narrower scope, better inputs, or a non-AI approach would meet the need.
Should this process use RPA, workflow automation, or an AI agent?
Not always an agent. If the rules are fixed and the inputs are already structured, deterministic workflow automation or a configuration change in software you own is usually the better fit — see the table above. An agent earns its cost when the work combines volume, judgment on messy inputs, and systems that don't talk to each other.
What drives the cost of building and running an AI agent?
Design and build costs scale with the integration surface, the state of your data, and how strict the acceptance thresholds are. Running costs include model usage, hosting, monitoring, and reviewer time in the queue, plus maintenance when a policy, vendor, or source system changes. We model both with you before building starts.
How do you keep a human in the loop for payments, approvals, and other high-risk actions?
Each agent's access follows least privilege, and any action that can move money or change a record of consequence needs an approval step your process owner defines. Expanded autonomy is earned action by action, only after thresholds are met, and every step stays reversible. Our guide to human-in-the-loop AI governance, linked above, goes deeper.
What access will your team need to our systems, and how is it scoped and revoked?
Access is granted by your team, per connector, and documented in the written scope — starting read-only, with write access added only where the workflow requires it. Credentials and scopes are reviewed with your security team as part of design, and revocation is part of the handover your team controls.
What do we own at the end: code, prompts, evaluation sets, accounts?
You own the implementation: architecture and configuration documentation, runbooks for likely failures, the evaluation sets and how to re-run them, and trained operators. Model-provider and hosting accounts are typically billed to you directly, so no vendor-held account stands between your team and the system.
Capabilities
- AI agent architecture
- Multi-agent workflow systems
- RAG and business data access
- Document intelligence and extraction
- CRM/ERP workflow execution
- Voice and chat-based operational agents
- Legacy-system integration and workarounds
How we work
- 01AI Agent Opportunity Audit
- 02Workflow and systems diagnosis
- 03Agent architecture and implementation roadmap
- 04Prototype or MVP
- 05Production deployment
- 06Monitoring, improvement, and expansion
Who this is for
Best fit
Companies where the C-suite knows AI matters, but needs a practical path from strategy to deployed agent systems.
Not a fit
- Generic chatbot requests
- Isolated AI experiments
- Companies looking for AI theater without operational ownership