Automatic.co is an AI automation company. We design, build, and govern AI agents and agentic AI systems that complete real business workflows end to end: reading the request, deciding the next step, acting in your ERP, CRM, and ticketing tools, and handing off to a person exactly where your risk policy says to.
The three terms get used interchangeably, but they describe different amounts of autonomy. Picking the right one for a workflow is most of the difference between an AI project that ships and one that stalls in pilot.
AI automation is the use of machine learning and large language models to run business processes that used to need human judgment: reading documents, classifying requests, extracting data, and drafting responses. Unlike rule-based automation or RPA, it copes with unstructured inputs and variation, so it can take on work that never fit a fixed script.
An AI agent is software that pursues a goal by planning steps, calling tools, and checking its own results, rather than answering a single prompt. Given a task like "reconcile this invoice", an agent decides what to look up, queries the ERP and bank feed, compares the records, and either posts the match or escalates the exception to a person.
Agentic AI is the broader system design in which one or more AI agents operate with delegated autonomy over a whole workflow: planning, coordinating with other agents, remembering context, and acting across many systems over time. It is what turns a single helpful agent into a governed operation, with orchestration, guardrails, and an audit trail around every decision.
| Rules / RPA | AI automation | AI agent | Agentic AI system | |
|---|---|---|---|---|
| Inputs it handles | Structured, predictable | Unstructured text, documents, email | Open-ended tasks | Whole workflows across teams |
| Who decides the steps | A developer, in advance | A developer; the model fills in judgment | The agent, within its tools | Coordinated agents, under policy |
| Acts in your systems | Yes, by script | Usually hands off to a person | Yes, through permitted tools | Yes, across many systems |
| When it breaks | On any change | On low-confidence inputs | Escalates when unsure | Routes exceptions, retries, escalates |
| Best for | Stable, high-volume clicks | Classification and extraction | Multi-step tasks with clear goals | End-to-end processes with handoffs |
We build the agent, the systems around it, and the controls that make it safe to give it real work. Every engagement ships to production in your environment.
Agents that complete multi-step work end to end, such as reconciling, triaging, filing, and updating records, instead of drafting text for someone else to finish.
02Specialized agents for research, execution, and verification that hand work to each other under an orchestrator, so no single agent carries the whole process.
03Approval gates, confidence thresholds, and review queues placed exactly where the stakes or the regulators demand a person, and nowhere else.
04AI automation that orchestrates long-running processes across every system of record, with retries, timeouts, and state that survives a restart.
05Assistants embedded in the tools your team already uses, grounded in your documents and permissions rather than the open internet.
06Recommendations built on your data with the reasoning and sources shown, so a person can approve the call in seconds and defend it later.
07Models and retrieval that run inside your perimeter, on-prem, in your VPC, or fully air-gapped, so sensitive data never leaves your control.
08A governed action layer that lets agents read and write SAP, NetSuite, Salesforce, ServiceNow, and the in-house systems nobody else will touch.
The best first workflow is high-volume, rules-heavy, and painful, with a clear definition of done. These are the ones we are asked to automate most.
Matches, posts, and reconciles in the ERP.
Approves exceptions above a set amount.
Classifies, resolves, or routes with full context.
Takes escalations and edge cases.
Drafts redlines and files findings with lineage.
Makes every legal judgment call.
Researches, updates the CRM, and prepares quotes.
Owns the relationship and the price.
Diagnoses and runs approved runbooks.
Approves production changes.
Chases, updates, and reschedules across systems.
Decides on substitutions and spend.
A production AI agent is a small distributed system. The model is one component; most of the engineering is in what surrounds it.
The model (blue) is one layer of six.
We start with a single workflow that has a measurable baseline, prove the agent against it, and only then widen its autonomy and add the next one.
We map candidate workflows and score each for volume, variability, risk, and data access. You leave with a ranked shortlist and a baseline for cost, cycle time, and error rate.
We design the agent topology, the tools it may call, the guardrails, and the human checkpoints that stay. Model and framework are chosen for the job, not the other way round.
We integrate with your systems of record and ship to production in shadow mode first, comparing the agent's work against your team's before it acts on its own.
We monitor quality, cost per task, and exception rate, tune prompts and tools, and raise the agent's autonomy level only when the numbers earn it.
Most agent demos send your data to someone else's cloud and act without a record. We design for your perimeter, your compliance team, and your auditors from the first call.
Run models inside your VPC, on your own GPUs, or fully offline. Data and prompts never leave your walls.
VPC isolation, SSO, role-based tool permissions, and audit logs enforced at the model and the action layer.
Risk thresholds, approval gates, agent versioning, and lineage for every decision an agent makes.
The agent drafts; a person does everything.
The agent prepares the action; a person approves each one.
The agent acts on low-risk items; a person reviews a sample.
The agent acts by default and escalates only exceptions.
The agent owns the workflow within hard policy limits.
Most workflows ship at L1 or L2 and earn their way up as the exception rate falls.
Production deployments. Some client names are withheld under NDA; the workflows and the numbers are real and available on a reference call.
An agent matches invoices to POs and bank feeds across three systems and writes the result back to the ERP, routing only true exceptions to AP staff.
A support agent reads, classifies, and resolves or routes inbound tickets, cutting first response from hours to under a minute.
A retrieval-grounded agent extracts clauses, flags deviations from the playbook, and drafts redlines for counsel to approve.
We pick the model and orchestration layer that fit the workflow, your data rules, and your cost ceiling, and we design so you can swap them later.
An agent is worth building when it moves a number you already track. We baseline before we build and report the delta after, in your terms.
Engagements are scoped per workflow after the assessment, with a fixed build phase and a run-and-improve phase. We will tell you when a workflow is better served by plain automation or not automated at all.
LegalBanking and lendingInsuranceClinical operationsAccountingPrivate equityManufacturingSupply chainEnergyReal estateRetail and ecommercePublic sector
RPA follows a fixed script of clicks and keystrokes and breaks when a screen or format changes. AI automation uses language models to read unstructured inputs such as emails, PDFs, and free-text requests, and to make the judgment calls a script cannot. In practice we often keep stable RPA steps and put AI where the variation is.
An AI agent is a single piece of software that plans and uses tools to reach a goal. Agentic AI describes the whole system design: one or more agents with delegated autonomy, plus the orchestration, memory, guardrails, and oversight that let them run a workflow reliably.
Generative AI produces content, such as text, code, or images, in response to a prompt. Agentic AI uses generative models as a reasoning engine but adds goals, tools, and actions, so the output is completed work in your systems rather than a draft for someone to act on.
A chatbot answers questions in a conversation. An agent takes actions: it looks up records, updates systems, sends messages, and checks its own results. Many of our agents have no chat interface at all; they run in the background on a queue of work.
When the process is fully structured and stable, plain automation is cheaper and more predictable. When there is no clear definition of done, or no way to check the agent's work, we recommend fixing the process first. The assessment says so plainly.
Most first deployments are a single agent with good tools. We move to multi-agent systems when a workflow has distinct roles, such as research, execution, and verification, or when one agent's context would get too large to stay reliable.
Every action an agent can take has a risk tier. Low-risk actions run automatically, higher-risk ones wait at an approval gate, and anything below a confidence threshold goes to a review queue with the agent's reasoning attached. Autonomy is raised only when the data supports it.
Yes. We deploy in your VPC, on your own GPUs, in hybrid setups, or fully air-gapped with open-source models. Retrieval respects your existing document permissions, and nothing is used to train a third-party model.
Yes. Agents act through a governed integration layer over APIs, databases, and where necessary the user interface of older systems. We have connected agents to SAP, NetSuite, Salesforce, ServiceNow, and in-house platforms.
The Model Context Protocol is an open standard for exposing tools and data to AI models. It is useful when many agents or assistants need the same tools. We use it where it simplifies permissions and reuse, not as a requirement.
We baseline the workflow before building, then track straight-through rate, accuracy against a reviewed sample, cycle time, cost per task, and the quality of escalations. Every agent ships with evaluations and traces so a regression shows up before a customer notices.
Assessment takes about a week. A first production agent typically runs in shadow mode within weeks and starts acting on live work once it matches your team's accuracy. Timelines depend on system access and the risk level of the workflow.
Tell us the process that eats the most hours. We will map it, score it for AI automation, and show you the agent architecture and the path to production. No deck, no drip sequence, just engineers.