Production AI agents that connect to your existing systems, call APIs, process documents, and execute multi-step workflows — with approvals, guardrails, and observability built in. Built for workflows that have to be right, not demos that look impressive.
Agents are worth it when a workflow spans systems, needs judgement at a few points, and happens often enough that people resent it.
An email arrives, a person reads it, and keys the same information into a CRM or ERP. Predictable, repetitive, and a steady source of errors.
No single system owns the workflow, so the handoffs live in people's inboxes and nothing can report on the end-to-end state.
Invoices, orders, or reports queue up waiting for someone to read, classify, and forward them to the right place.
You built the scripted version, and it fails whenever the input varies slightly from the format it expected.
Qualifying leads, checking eligibility, or gathering context before a decision is a full job that is mostly lookup and summarisation.
The same multi-source report gets assembled by hand every week because no tool spans all the sources involved.
Each of these is a real execution path, not a chat interface over your data.
Read inbound communications, extract what changed, and write it back to the record — with the update logged and reversible.
Classify what arrived, pull the fields that matter, validate them, and hand off to the system that owns the next step.
Gather context on an inbound lead from your own data and public sources, score it, and route it with the reasoning attached.
Pull from the systems that hold the pieces, reconcile them, and produce the report on a schedule instead of on request.
Classify incoming issues, attach the relevant history and documentation, and route to the right queue with context.
Check submissions against policy, surface what needs a human decision, and record why each item was passed or flagged.
Agents are not an upgrade to every automation. They cost more to build and operate, and they are the wrong answer for deterministic work.
| Rules / RPA / scripts | AI agents | |
|---|---|---|
| Input format | Fixed and predictable | Varies in wording, layout, or structure |
| Decision logic | Fully specifiable up front | Needs interpretation or judgement at some steps |
| Failure mode | Breaks loudly when input changes | Degrades — needs confidence scoring to catch |
| Cost to run | Near zero per execution | Per-token cost that scales with volume |
| Auditability | Trivially explainable | Requires deliberate logging of each step and tool call |
| Best fit | High-volume, identical, well-defined tasks | Variable inputs where a person currently reads and decides |
An agent that cannot act on your systems is a chatbot. Integration and permission design is the majority of the build.
CRM & Sales
ERP & Finance
Communication
Workspace & Storage
Custom & Protocol
Full autonomy is a design choice per step, not a property of the system. The interesting question is which actions require sign-off.
Sending external communication, moving money, or changing a customer record can require explicit sign-off while the rest of the workflow runs unattended.
When the agent is not sure, the item routes to a person instead of guessing. The threshold is tuned against your own data, not a default.
Actions are logged with enough context to be undone. Where a step cannot be reversed, it sits behind an approval gate by default.
Each tool call, input, and decision is recorded, so you can reconstruct exactly why the agent did what it did months later.
The framework is the least interesting layer. These are the parts that determine whether it survives production.
The workflow graph that decides which step runs next, holds state across steps, and resumes cleanly after a failure instead of restarting from scratch.
Typed, validated interfaces to your systems, with schema checking on both the call and the response so a malformed action never reaches production data.
The agent acts under scoped credentials with least privilege, so what it can touch is bounded by configuration rather than by prompt instructions.
A test set of real cases the agent must still handle correctly, run before every change, so improvements to one path do not silently break another.
Full traces of each run — inputs, tool calls, outputs, cost, and latency — so failures are diagnosable rather than mysterious.
Defined behaviour when the model is unavailable, the confidence is low, or a tool errors, so the workflow degrades to a queue rather than to silence.
Agents get scoped service accounts limited to the specific records and actions the workflow needs, so a prompt injection cannot escalate into broad system access.
Where data cannot leave a region or a boundary, we route to models that satisfy that constraint, including self-hosted options where required.
Untrusted content from emails and documents is treated as data rather than instruction, with tool permissions enforced outside the model rather than inside the prompt.
What is logged, for how long, and who can read it is decided during design — including redaction of sensitive fields from traces.
We map the current process end to end, identify which steps genuinely need judgement, and check whether an agent is the right answer at all.
One workflow, instrumented and evaluated, running alongside the manual process so you can compare outputs before relying on it.
Confidence thresholds tuned on real cases, approval gates set where you want them, and observability you can read without us.
Additional workflows added once the first is trusted, reusing the integration and permission layer already built.
Framework choice follows the workflow. It is an implementation detail, not the reason an agent works in production.
A workflow automation assessment maps the process end to end and tells you which steps genuinely warrant an agent — including when scripted automation would be cheaper and more reliable.