A chatbot answers. An agent acts. That is the whole distinction, and it is the difference between a system that tells your customer what the refund policy says and one that checks the order, applies the policy, issues the refund and updates the ticket.

Chatbot versus agentic system

A conventional chatbot takes a message, retrieves some text, produces a reply, and forgets. It has one step and no hands. An agentic system is given a goal rather than a question. It decides what needs to happen, chooses which tools to call, reads the results, revises its plan when a step fails, and keeps going until the goal is met or it hits a limit you set. It has memory across steps, access to your systems, and — critically — a defined boundary on what it may do without asking a human first.

That capability is exactly why it needs more engineering, not less. An agent that can act can also act wrongly, at speed, repeatedly.

Who this is for

Companies where the expensive work is not answering questions but carrying out multi-step processes: reconciling records across systems, triaging and resolving tickets, researching and enriching leads, processing documents into structured actions. If a competent new hire could do it from a written procedure, an agent can usually be built for it.

Problems we solve

Agents with no brakes. Autonomy granted without spend limits, step limits or approval gates.
Undebuggable behaviour. Something went wrong three tool calls deep and there is no trace of the reasoning or the inputs.
Tools that lie to the model. Vague function descriptions and sloppy error returns, so the agent picks the wrong tool and cannot tell it failed.
Infinite loops. An agent retrying the same failing call until the budget is gone.
No idempotency. A retried step that sends the email or charges the card a second time.

What we build

Goal-driven agents with explicit planning, tool selection and self-correction on failure
Tool layers over your APIs, databases and SaaS platforms, with typed inputs and honest error returns
Human-in-the-loop approval on any action that spends money, contacts a customer or changes a record
Persistent memory across sessions, so the agent knows what it already did
Guardrails: step ceilings, token budgets, allow-lists, timeouts and circuit breakers
Full execution tracing — every prompt, tool call, result and decision, retained and searchable

How we work

We write the procedure as a human would follow it before writing any code, because an agent cannot execute a process nobody has articulated. We then build the tools first and test them independently — most agent failures are tool failures wearing a disguise. Autonomy is granted incrementally: the first version proposes and a human approves, and we widen the boundary only where the logs show it has earned it.

Technologies we use

Claude, GPT and Gemini for reasoning, with Ollama where the data cannot leave your network. LangChain and LlamaIndex for orchestration, the Model Context Protocol for standardised tool access, Qdrant and pgvector for retrieval, Redis for memory and locking, and n8n where a visual workflow is the better home for the orchestration.

Business benefits

Multi-step processes complete without a person shepherding each stage
Work continues overnight and across time zones
Procedures are followed identically every time, with an audit trail
Staff move from executing the process to handling the exceptions

Where it pays off

Support tickets triaged, researched and resolved end to end
Inbound leads enriched, scored, routed and followed up
Records reconciled across CRM, billing and accounting
Documents read, validated and turned into system actions
Recurring reports researched and assembled from several sources

Common questions

How much autonomy is safe?

Start with none. We ship agents that propose actions for human approval, watch the approval rate, and remove the gate only on action types where the logs justify it. Anything irreversible — payments, deletions, external messages — usually keeps its gate permanently.

What if the agent does something wrong?

Every action is logged with its full reasoning chain, so the failure can be reconstructed. Destructive operations sit behind approval, spend and step limits cap the blast radius, and idempotency keys prevent a retry from doubling an effect.

Is this just a wrapper around ChatGPT?

No. The model is one component. The engineering is in the tool layer, the state management, the guardrails, the evaluation harness and the observability — which is where the cost sits and where projects fail.

Have a multi-step process worth automating? Describe how a new hire would do it and we will tell you whether an agent can.

Agentic AI Development