AI Agent Consulting for Business Workflows

We design and build AI agents that have a defined job, a defined toolkit, approval gates on anything that matters, and audit logs on everything. Triage, classify, draft, summarize, route, update. The work that used to take a person twenty minutes per ticket. Agents do not replace your team. They handle the steps that do not need a person, so the steps that do can get more attention.

An AI agent is a system that takes a multi-step task (read this, classify it, look up that, draft a reply, update a record, notify a person) and runs it with limited human supervision. The work that used to require a person sitting at a screen for twenty minutes is reduced to a person reviewing the result for thirty seconds.

An AI agent is a system with a defined job and a bounded toolkit that runs a multi-step task, read this, classify it, look up that, draft a reply, update a record, with limited human supervision. Thinklytics designs agents with approval gates on anything that matters and audit logs on everything, so a twenty-minute task becomes a thirty-second review.

A system with a defined job and a bounded toolkit. It can read these systems, write to those systems, and escalate in these cases.

Approval-gated where the action matters: customer-facing replies, record-of-truth changes, money movement.

Owned. Every agent has a named human owner who is accountable for outputs.

A person. Approvals and accountability stay with the human owner.

First production agent typically ships in 6 to 10 weeks for a scoped workflow.

Member match accuracy on three previously stalled ML pilots. Recovered $4.8M a year in misrouted claims.

Prior auth review with an intake, classification, and routing agent in a regulated workflow.

Revenue discrepancy resolved. Six conflicting metrics reconciled to one certified ARR definition.

It was built without grounding in your data, without classification logic, and without an approval gate.

There's no agent watching for duplicate detection or merge proposals.

A chatbot answers a question. An agent takes a multi-step task. Read, classify, look up, draft, route, update. It runs with bounded autonomy and a human approves the steps that matter.

No. The agent does the steps that don't need judgment so the person can focus on the steps that do. We don't ship agents that take customer-facing or record-of-truth actions without a human approval.

We ground outputs in your sources and log retrieval. We require human approval on outputs that touch customers or systems of record. For internal-only summaries, we surface confidence and cite sources.

Yes. We work inside your access controls and audit requirements. The BlueCross BlueShield Affiliate prior-auth case study is a regulated example.

Typical first production agent ships in 6 to 10 weeks for a scoped workflow. Some are faster.

You do. We hand over documentation, prompts, evals, and runbooks. If you want us to manage it long term, that's a separate retainer (Managed AI Operations).

An AI agent is a model given a goal, a set of tools it may call, and a boundary on what it can do without a human. In a business setting that usually means reading a case, pulling the relevant records, drafting an action, and either executing it inside a permitted range or escalating. The engineering effort sits almost entirely in the boundary and the audit trail, because an agent with unclear limits is a liability rather than a capability.

When should a business use an AI agent instead of a fixed workflow?

Use a fixed workflow when the steps are known and stable, because it is cheaper to build, easier to test, and it fails predictably. An agent earns its cost when the path varies case by case and writing every branch is impractical. A useful test is the exception rate. If a scripted process hands back more than roughly one in five cases for human judgment, the variability is real and an agent is worth scoping.

The model is rarely the expense. A narrow agent doing one task against one system, with a human approving its actions, is typically a 6 to 10 week build, and most of that is integration, permissions, evaluation, and the audit trail. Cost rises with the number of systems it must touch and with how much autonomy you grant it, because every increment of autonomy demands more testing and more guardrail work. Ongoing cost splits into inference, which is usually modest and predictable, and maintenance, which is not, because upstream systems change and evaluations have to be rerun. Budget for the second year, not just the build.

No two agent builds are the same, so scope is set before any number. These are the factors that move the effort.

One narrow triage agent differs from a fleet that classifies, drafts, routes, and updates across systems.

Each CRM, ticketing, BI, or warehouse the agent reads from or writes to adds integration and testing.

Actions that touch customers, money, or records of truth need gates, a kill switch, and audit logs, which is most of the safety work.

The evals, runbooks, and documented quality bar that keep the agent trustworthy scale with the stakes.

An agent reasoning on messy data needs a semantic or governance layer first, which can be the larger part of the work.

Every build starts with an agent design session that scopes the job before any number is discussed.

Off-the-shelf agent platforms make you adapt your process to their capabilities. Here is how a purpose-built agent differs.

Approval gates, a kill switch, and audit logs on anything that matters.

You need approval gates, audit logs, and a kill switch, not a black box.

You want to own the agent after launch, with runbooks and evals.

You mainly need answers from your dashboards in plain language: see Agentic BI Implementation.

The task is a fixed, rules-based workflow with no judgment: see AI Workflow Automation.

Your data is not yet trustworthy enough to reason on: start with Data Governance or a Semantic Layer.