AI engineering service
AI agents that execute defined workflows inside your systems.
We design and build agents that observe, plan, act, and verify within explicit boundaries — connected to your tools, logged end to end, and supervised until they have earned more autonomy.
The problem
Chat interfaces do not move work forward on their own.
A model that writes good text is not the same as a system that completes a task. Real workflows require reading from systems, following rules, taking actions, and knowing when to stop.
What we typically hear
- People copy answers out of AI tools and paste them into other systems by hand.
- Prototype agents work on clean examples and break on real inputs.
- No one can explain what an agent did, or why, after the fact.
- Security teams block agents because permissions are too broad.
- There is no way to measure whether an agent is getting better or worse.
What we build
What we build
Single-purpose agents
Agents scoped to one workflow — such as triaging requests or reconciling records — with a small, explicit tool set.
Multi-step orchestration
Workflows where an agent plans across several tools, hands off between steps, and resumes after human input.
Tool and API layers
Typed, permission-checked tools that wrap your systems so agents can only perform allowed operations.
Approval and escalation
Review queues, thresholds, and notifications that route uncertain or high-impact actions to a person.
Observability
Structured traces of every observation, decision, tool call, and outcome — searchable and exportable.
Evaluation suites
Test sets built from real cases that are run before every change, so behaviour is measured rather than assumed.
Typical use cases
Where this work pays off
Document intake
01Read incoming documents, extract structured fields, validate them against rules, and file them — flagging anything incomplete.
Request triage
02Classify inbound requests, gather context from connected systems, route them, and draft a first response.
Record reconciliation
03Compare records across two systems, resolve clear mismatches by rule, and queue the rest for review.
Recurring analysis
04Pull data on a schedule, produce a structured summary, and highlight changes that need attention.
Data quality remediation
05Detect duplicates, missing fields, or inconsistent formats and propose or apply fixes within defined limits.
Policy monitoring
06Check activity against written policies and raise exceptions with the evidence attached.
Architecture
The agent operating loop
- 1
Observe
Read authorized systems and the incoming task.
- 2
Plan
Choose the next step against rules and instructions.
- 3
Act
Call an approved tool or prepare a draft.
- 4
Verify
Check the result and record what happened.
- 5Human
Escalate
Route uncertain or out-of-policy cases to a person.
Technology considerations
Decisions we make with you
Technology choices follow from the workflow, data, and constraints — not the other way around.
Frameworks
We use established agent frameworks where they help and plain, testable code where they add complexity. The choice is driven by maintainability.
Model routing
Different steps can use different models — a capable model for planning, a faster one for classification — to balance quality and cost.
State and memory
Task state is stored explicitly in a database rather than relying on long conversation context, so runs can be resumed and audited.
Failure handling
Timeouts, retries, idempotent tools, and safe defaults so a failed step does not leave systems in a half-updated state.
Delivery
How the engagement runs
01
Scope
Define the workflow, inputs, allowed actions, and what success looks like.
02
Supervised build
Build the agent with every action requiring review, using real historical cases.
03
Evaluate
Measure accuracy, failure modes, and edge cases against an agreed test set.
04
Gradual release
Allow low-risk actions to run unattended once they consistently pass review.
05
Operate
Monitor, retrain prompts and rules, and expand scope based on evidence.
Security & governance
Control is designed in, not added later.
Every system we deliver includes the controls below as a baseline. Specific compliance requirements are scoped with your team.
- Least-privilege, scoped credentials per agent
- Allow-lists of tools and operations
- Approval thresholds for financial, legal, or customer-facing actions
- Complete audit logs of observations, decisions, and actions
- Evaluation suites run before every release
- Kill switch and rollback for every deployed agent
FAQ
Frequently asked questions
01How is an AI agent different from a chatbot?
A chatbot responds in a conversation. An agent works through a task: it reads from systems, decides on a next step within rules, uses tools to act, checks the result, and escalates when it should not proceed.
02Will the agent act without anyone checking?
Not at first. Agents launch in supervised mode where actions are reviewed. Specific low-risk actions move to unattended execution only after they have performed reliably against agreed criteria.
03What happens when the agent is wrong?
Errors are expected and designed for. Uncertain cases are escalated, actions are logged, high-impact steps require approval, and evaluation suites catch regressions before release.
04Which models and frameworks do you use?
We work with models from providers such as OpenAI, Anthropic, and Google, and choose frameworks per project. We avoid locking a system to one provider where that is practical.
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Next step
Bring us the process your team is tired of doing manually.
A strategy call is a working conversation about one workflow: what it involves today, what could be automated, and what a first build would look like.
What happens next
- 1Tell us about one workflow
- 2We review it before replying
- 3A working session, then a first-build proposal