Goal-oriented agents
An agent scoped to one job with measurable completion criteria, not an open-ended assistant.
Goal-driven autonomy
Agentic AI & AI Agents
Agents that pursue a goal, call your tools and complete multi-step work under explicit guardrails.
What this is
An agent differs from a chatbot in one respect: it takes action. We build agents with a defined goal, a bounded tool set, approval checkpoints on consequential steps and an escalation path to a person. Every action is logged with the reasoning behind it, so you can audit what happened.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- Work that spans four systems still needs a person to carry it between them.
- Automation breaks the moment a case deviates from the happy path.
- Nobody will approve autonomous software without an audit trail and a stop button.
- Multi-step processes depend on individual memory and stall when someone is away.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- Agent architecture with an explicit goal, tool specification and success criteria
- Working agent deployed in your environment with access scoped to what it needs
- Tool and API integrations into the systems the work actually touches
- Approval checkpoints on consequential actions, with escalation to a named owner
- Guardrails: permission scoping, rate limits, spend caps and refusal behaviour
- Audit log of every action, input and decision, queryable after the fact
- Evaluation suite covering happy path, edge cases and failure handling
- Observability dashboard: success rate, cost, latency and escalation volume
Capabilities
What the system does
Tool calling and API integration
Typed tools with validated inputs, least-privilege credentials and predictable error handling.
Workflow orchestration
Durable multi-step execution with queues, retries, timeouts and resumption after failure.
Multi-agent patterns
Separate agents per bounded responsibility where that genuinely reduces complexity, not by default.
Approval checkpoints and guardrails
Consequential steps pause for human sign-off; limits and refusals are enforced in code, not prompts.
Audit logs, testing and monitoring
Full action history, regression tests on every change and alerting when behaviour drifts.
Applied
Where it is used
Each of these is a bounded job with a measurable result, which is how we prefer to scope a first build.
01
Back-office operations agent
Reads incoming requests, updates records across systems and flags only genuine exceptions.
02
Data reconciliation agent
Compares records across platforms nightly, corrects drift within limits and raises evidenced tickets.
03
Onboarding agent
Walks a customer or employee through each step, chasing missing documents and provisioning access.
04
Research and briefing agent
Gathers and reconciles sources, then returns a cited brief in your template.
05
Procurement and approval agent
Prepares requests, routes them for approval and follows up until the step clears.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
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01
Goal and policy definition
What the agent may do, what it must never do, and when it must ask a person.
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02
Tool layer
Typed, validated tools wrapping your APIs and databases under scoped credentials.
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03
Orchestration
Planning and execution with durable state, queues, retry logic and resumption.
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04
Approval gates
Human sign-off on consequential actions, with the full context presented for the decision.
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05
Memory and context
Task state and relevant retrieved context, scoped to the current job.
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06
Observability
Action log, traces, evaluation runs, cost tracking and drift alerting.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Least-privilege credentials per tool; the agent holds only the access its job requires.
- Approval gates on irreversible or financially consequential actions, configurable per action type.
- Spend caps and rate limits enforced in code, independent of model behaviour.
- Complete audit trail of actions, inputs, outputs and reasoning, retained for review.
- A stop control that halts execution immediately without leaving work half-applied.
- Prompt-injection defences on any content the agent ingests from untrusted sources.
Delivery
Our approach
How we run the engagement
Discovery
Process walkthroughs, data review, constraints and a written scope you sign off before build.
Architecture
Data model, integration boundaries, hosting and the security model, agreed up front.
Proof of concept
A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.
Development
Two-week increments, each ending in a working deployment and a review.
Testing
Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.
Deployment
Staged rollout into your infrastructure with monitoring and a tested rollback path.
Monitoring & support
Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.
Proven work
Products we have built and sold
We are product builders as well as a services team. These are our own commercial platforms, not client references.
Digital sales & support agent
SIZH Virtual Agent
A virtual digital agent for sales, support and reception — trained on your business, live on WhatsApp, web, voice and email.
View product Mobility platformRideOn
A ride-hailing and fleet management platform, now with agentic dispatch, driver support and demand intelligence.
View product Multivendor marketplaceUniBooker
A configurable multivendor service-booking marketplace with agentic onboarding, matching and support.
View product
Questions
Agentic AI & AI Agents — FAQs
The questions we are asked most often about this service.
How much autonomy should an agent have?
Less than most teams expect at first. We start with the agent proposing actions and a person approving, then widen autonomy per action type once the audit log shows it is reliable. Irreversible steps usually keep their approval gate permanently.
What stops it doing something damaging?
Scoped credentials, approval gates, spend caps and rate limits, enforced in code around the model rather than requested in a prompt. The agent cannot call a tool it was not granted.
How do you test an agent?
A fixed scenario suite covering the happy path, known edge cases and deliberate failures, run on every change. We also replay real historical cases and compare the agent against what actually happened.
Do we need multiple agents?
Usually not at first. One well-scoped agent is easier to debug and trust. We add agents when responsibilities are genuinely separate, not to make the architecture look sophisticated.
What happens when it gets stuck?
It escalates to a named owner with the task state, what it tried and why it stopped. Silent failure is the one outcome we design hardest against.
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