Agentic AI

AI agents built for real work.

Agentic systems can move beyond answering questions and begin executing workflows — reasoning about tasks, interacting with tools and coordinating actions across systems.

The challenge is making them reliable enough for production.

Argbit designs agent architectures around explicit boundaries, observable behaviour and appropriate human control.

Explore an agentic AI opportunity

What we build

Agents with a defined job and a defined boundary.

Operational agents

Agents that execute structured business processes.

Knowledge agents

Agents that research, synthesise and reason across enterprise knowledge.

Customer operations agents

AI systems that assist or automate service workflows.

Developer agents

AI systems supporting engineering, testing, operations and internal platforms.

Multi-agent systems

Specialised agents coordinating across complex tasks.

System architectureEvents flowing

entry

User / event

control

Orchestratorstate machine

reasoning

Plannerbounded

execution

Specialist agents

Tools / APIs

Enterprise systems

control plane

Evaluation

Observability

Human oversight

Reference architectureControl flow stays deterministic; reasoning is contained within a step.

Architecture

Boundaries make agents dependable.

Every tool is typed and authorised server-side. Every action above a risk threshold routes to a human. Every run produces a trace that an engineer can reconstruct.

agent.config.ts

export const servicingAgent = defineAgent({
  tools: ["policy.read", "note.append", "ticket.create"],
  memory: { scope: "case", ttl: "24h" },
  boundary: { requiresApproval: (a) => a.writesToLedger },
  budget: { maxToolCalls: 10, maxUsd: 0.35 },
  eval: "suites/servicing.v3",
});

Have a workflow you think an agent could run?

Send us the process. We will tell you which parts should be deterministic, which parts benefit from reasoning, and where a human needs to stay in the loop.

Explore an agentic AI opportunityNo AI theatre. Just engineering.