What This Is
AI agents are autonomous processes that execute repeatable business tasks end to end, without a human triggering each step. Unlike AI features embedded in an interface, agents operate independently: they receive a trigger, follow a defined process, use tools, make structured decisions, and produce an output or take an action. Your team reviews the result, not the process.
An agent does not wait for a user to type a prompt. It runs because a business event happened: a form was submitted, a deadline passed, a dataset was updated, a schedule fired. It then handles the work that would otherwise sit in someone’s task list.
We build agents on our own Beacon Agents framework, which provides the orchestration layer: tool registration, execution control, state management, and output handling. The same framework powers the agents running in production inside our own business.
When You Need This
Agents become valuable when your business has repeatable, multi-step processes that a person currently handles but that do not require human judgement. The tell-tale sign: someone on your team spends hours each week running the same sequence. They pull data, format it, make a low-complexity decision, update a system, and send a notification. The outcome is predictable 90% of the time.
Specific scenarios:
- Processing incoming requests: an agent triages, categorises, and routes submissions based on defined criteria, escalating only the exceptions
- Content production workflows: an agent generates drafts from structured data, applies style guidelines, and queues the output for human review
- Monitoring and alerting: an agent watches for conditions across systems and takes predefined actions when thresholds are breached
- Scheduled data operations: an agent pulls data from multiple sources on a schedule, transforms it, and produces a report or updates a dashboard
If the process requires creativity, subjective judgement, or high-stakes decision-making, an agent is not the right solution. Agents are best applied to volume, consistency, and speed: doing the predictable work so your team can focus on the work that is not.
How We Work
Agent development starts by mapping the process the agent will replace: every step, every decision point, every input and output. This matters more than the technology choice. An agent that automates a bad process just automates the bad outcomes faster.
Each agent is built as a set of discrete tools: small, focused capabilities (read data, call an API, generate content, send a notification), connected by orchestration logic that decides which tool to use and in what order. This modular design means:
- Individual tools can be tested, replaced, and improved independently
- The agent’s decision-making is auditable: you can trace exactly why it did what it did
- New capabilities can be added without rewriting the agent
Development follows the same iterative process as all our work, with additional dry-run testing: the agent runs against real data in a sandbox environment before it touches production. We tune the agent’s behaviour, adjust the tool chain, and define the escalation rules (when to stop and hand off to a human) before it goes live.
What You Get
- Autonomous agents that execute defined processes without manual triggering
- Modular tool architecture: discrete capabilities that are independently testable and extendable
- Audit trail for every agent execution: what it did, which tools it used, and what output it produced
- Escalation rules with clear boundaries for when the agent should stop and hand off to a human
- Monitoring dashboard showing agent activity, success rates, and exceptions
- Usage-based pricing: agents run on a base subscription plus per-execution costs, so you pay for what runs
Technologies We Use
- Beacon Agents framework for orchestration, tool management, and state control
- OpenAI API for language understanding and structured decision-making within agent tools
- Laravel for job queuing, scheduling, and integration with business systems
- PostgreSQL for execution logs, state persistence, and audit data
Related Systems
Agents typically operate within or alongside existing systems rather than replacing them. A query management system might use an agent to auto-triage incoming requests. A reporting dashboard might use an agent to generate weekly summaries. The agent handles the repeatable work; the system provides the interface and data layer.
This work sits alongside our business automation service and the AI development work we do to embed intelligence into products. You can see the full range of what we build on the services overview.
Talk to Us About Automating a Process
If your team has a process that runs the same way every time and you are paying someone to do it manually, get in touch and we will assess whether an agent is the right approach.
Doing, Not Saying
An agent is given a goal rather than a script, and can take actions to pursue it: reading records, calling systems, deciding what to do next based on what it finds.
A chatbot answers. An agent looks up the order, checks the stock, issues the refund and emails the customer.
That distinction is the entire commercial story. Answers can be ignored. Actions have consequences, which is why the interesting question about any agent is not how capable it is but what it is allowed to do without asking.
The Conversation We Start With
Permissions, before capability.
For every action the agent could take, there are three possible answers: do it, do it and tell somebody, or ask first. Getting that list agreed before anything is built is what separates an agent that saves work from one that creates an incident.
The rule we apply: anything irreversible is gated by design, not by instruction. An agent does not distinguish sending an internal note from emailing four hundred customers; both are just available actions. Instructions are guidance. Permissions are enforcement.
Where Agents Genuinely Work
Repetitive, rule-shaped work spanning several systems that do not talk to each other.
Gathering information scattered across tools into one place. Triaging incoming requests by working out what each is about and routing it. Monitoring for a condition and acting when it occurs. Handling the first stage of a request end to end and passing anything unusual to a person.
The common thread: a person could do it, it takes them a while, it needs judgement too fuzzy for simple rules, and being occasionally wrong is survivable.
Where They Fall Over
Long chains. Each step carries a chance of drifting, and errors compound. An agent taking twelve steps has many opportunities to go slightly wrong and will pursue the drifted goal with the same confidence. Short chains with checkpoints are far more reliable than long autonomous runs.
Edge cases. Agents handle the common path well and the unusual case confidently and wrongly. The unusual case is frequently the one that mattered.
Silent inactivity. An agent quietly doing nothing looks identical to one with nothing to do.
Scope creep by prompt. Widening what it handles by editing instructions rather than re-scoping is how agents end up doing things nobody signed off.
What Moves The Price
How many systems it touches, and whether those have usable APIs.
How many actions are irreversible, because each one needs a gate and a record.
How long the chains are. More steps means more checkpoints and more testing.
Whether it talks to customers. A step change in review requirements.
How much observability you need. For anything consequential, a readable record of what it did and why is not optional.
Questions To Ask Whoever Builds One
- What can it do without asking? Ask for the list, in writing.
- Can it spend money, contact customers, or change records? Each deserves a separate deliberate answer.
- What does it do when it is unsure? Stopping and escalating is correct behaviour and has to be built.
- How would we know it was doing the wrong thing?
- How do we undo a run? Sometimes trivial, sometimes impossible. Worth knowing which beforehand.
- What is the smallest useful version?
A Brief You Can Send Anyone
The job: [what a person does today, step by step]
How often: [and how long it takes them]
Systems it must reach: [and whether they have APIs]
Actions that are safe to automate: [your list]
Actions that must be approved: [your list, and by whom]
What must never happen: [the failure that would genuinely hurt]
Who reviews what it did: [and how often]
Volume: [how many times a day]
The Honest Framing
Agents are not staff replacements, and the pitch that says so is the one to be wary of. What they are is a way to automate work that was previously too fuzzy for rules and too small to justify somebody’s full attention.
The failures we see are rarely the model being incapable. They are scope: an agent given a goal broader than anyone thought through, with permissions wider than the goal needed, and nobody watching what it did.
Start narrow, reversible and observed. Widen it once it has earned that. We build these as Beacon Agents, and where the work is producing text rather than taking action, AI development is the better starting point.
Get in touch with the job, and we will start with what it is allowed to do.
