AI Agents and Agent Systems

AI agents that do the work, under your control.

An agent is only as useful as the system around it. Metacto engineers agents that research, prepare, and act across your business systems, with defined tools, scoped permissions, and a person at every decision that matters.

45 minutes with our AI experts to find your highest-value opportunity and a practical next step. No preparation required.

Talk to an expert Explore Custom AI Development

For leadership and engineering teams that want AI to complete real tasks across their systems, not just answer questions in a chat window.

When an agent system is the right tool

A single prompt handles a single answer. Agents earn their place when the work spans several steps, several systems, and a decision at the end.

The work crosses several systems

Preparing the job means pulling from the CRM, the document store, and the inbox before anyone can act.

Steps depend on earlier results

What happens next depends on what the last step found, so a fixed script breaks on the first exception.

Actions carry consequences

Sending, updating, or submitting needs clear permissions and a person who approves before it happens.

Nobody can see what the AI did

Without logs and traces, a wrong output cannot be explained, corrected, or prevented next time.

What Metacto builds

Each technical element exists for a business reason. This is the system around the model.

Tool definitions: each action the agent can take is named, typed, and documented, so behavior stays predictable.
Scoped permissions: agents read and write only what the role requires, matched to the people they work for.
Orchestration: multi-step and multi-agent work is planned, sequenced, and recovered when a step fails.
Research and preparation: agents gather context across systems and assemble work a person can review quickly.
Human review points: consequential actions pause for approval, with the evidence the reviewer needs.
Earned autonomy: agents move from draft, to recommend, to submit with approval as quality is proven.
Logging and traces: every tool call, input, and output is recorded, so any result can be explained.
Clear stopping rules: agents escalate when information is missing or confidence is low, instead of guessing.

How agents are built and tested

Every agent is designed around a defined job, tested on real cases, and given more responsibility only as it proves reliable.

01Design

Define the job and the boundaries

We specify what the agent is responsible for, which tools it can use, which permissions it needs, and where a person must approve.

A written agent specification your team can review before anything is built.

02Build

Build the tools and the orchestration

We implement the tool definitions, connect them to your systems, and design how steps and agents hand work to each other.

A working agent system running against your real environment in a controlled setting.

03Prove

Test, then extend autonomy

We run the agent on real cases against agreed acceptance criteria, review traces with your team, and widen its scope only where results support it.

An agent in use with a documented level of autonomy and a clear path to the next one.

Example: what an agent specification covers (illustrative)

A simplified outline of the document we agree on before building an agent. Shown for illustration, not taken from a client engagement.

Job statement: the task, who it serves, and what a finished result looks like
Inputs: the systems and records the agent reads, and the access each requires
Tools: each permitted action, its parameters, and what it may change
Autonomy level: draft, recommend, or submit with approval, per action
Review points: which steps pause for a person, and what evidence they see
Escalation rules: when the agent stops and hands the case to a person
Acceptance criteria: the test cases and quality bar required before launch
Trace requirements: what is logged for each run and who can review it

Controls

Agents that act within limits you set

Control is designed into the agent from the first specification, not added after launch.

Permissions

Access scoped to the role

Least-privilege access to each connected system
Separate read and write permissions per tool
Credentials managed outside prompts and code

Oversight

People approve what matters

Approval steps before consequential actions
Autonomy extended only as quality is proven
Clear escalation when the agent is unsure

Visibility

Every run can be explained

Traces of each tool call, input, and output
Logs your team can review and retain
Results tied back to the sources used

We confirm system access, data handling, and approval requirements with your IT and security owners before any agent is connected.

Common questions

What is the difference between an agent and a chatbot?

A chatbot answers questions. An agent completes tasks: it gathers information from your systems, decides on the next step, and takes defined actions, with a person approving where the action carries consequences.

Will agents take actions without approval?

Only where you decide they should. Agents start by drafting work for review. As results prove reliable on real cases, specific actions can move to recommend, then to submit with approval. Each step is a deliberate decision your team makes.

When do you use multiple agents instead of one?

When the work has distinct stages that benefit from separate responsibilities, such as research, preparation, and review. Splitting the work makes each part easier to test and explain. When one agent is enough, we keep it to one.

How do we know what an agent did?

Every run produces a trace: the inputs it received, the tools it called, the information it used, and the output it produced. Your team can review any result and see how it was reached.

Which models and frameworks do you use?

We choose models and frameworks per system, based on the task, your environment, and your security requirements. Agent systems can also run on Vocion, Metacto's open-source operating layer, so later agents build on the same foundations.

Which service includes this capability?

Custom AI Development is where most clients put this capability to work. AI Implementation and Managed AI Services also rely on it when the system being delivered or operated includes agents.