Agents that do the work — not just chat.
An AI agent is not a chatbot with a better prompt. It is a system that pursues a goal — planning, calling tools, using memory and acting — under the guardrails you control. We design, build and operate agentic systems reliable enough for production.
The agent loop.
Every real agent runs the same cycle. Get this right and everything else follows; get it wrong and you have a chatbot pretending to be a system.
Goal
A clear objective and success criteria the agent can reason about.
Plan
The model decomposes the goal into steps and picks the next action.
Act
It calls a tool — an API, function, query or code — to affect the world.
Observe
It reads the result and checks progress against the goal.
Remember
State is written to short- and long-term memory for the next step.
Stop or hand off
It completes, escalates to a human, or loops — within strict bounds.
Agent architecture.
A production agent is a small system, not a single prompt. These are the layers we design and harden.
Control & orchestration
Planner, routing, guardrails, human-in-the-loop
Reasoning core
The LLM, with structured output and prompting
Tools & actions
How the agent affects the world
Memory
Short-term context and long-term recall
Knowledge & retrieval
Grounding the agent in your data
Observability & evaluation
How you trust it in production
Workflow, single agent, or multi-agent?
More autonomy is not better — it is riskier. We choose the simplest architecture that solves the problem.
Deterministic workflow
Best for: Known, fixed steps
- No agent needed
- Cheapest and most reliable
- LLM used only for specific steps
- Fully predictable
Single agent
Best for: Open-ended tasks, one domain
- One reasoning loop
- A focused set of tools
- Simplest agentic option
- Easy to evaluate and trace
Multi-agent
Best for: Complex, cross-domain work
- Supervisor + specialist agents
- Clear handoffs and roles
- Scales to hard problems
- More to orchestrate and govern
The autonomy ladder.
We start low on the ladder and earn autonomy with evidence. You decide how far up any given workflow goes.
Assist
The agent suggests; a human does. Zero autonomous action.
Supervised
The agent acts, but every consequential step needs approval.
Bounded autonomy
It acts freely within hard guardrails, limits and allow-lists.
Autonomous
Self-directed within its domain, monitored and reversible.
Agents that earn their keep.
We deploy agents where judgement and tool-use meet — not to automate a spreadsheet.
Sales agent
Researches accounts, drafts outreach, updates the CRM.
Operations agent
Triages tickets, runs playbooks, coordinates systems.
Support agent
Resolves requests end-to-end, escalating when unsure.
Analytics agent
Answers data questions and builds the query for you.
Knowledge agent
Finds, synthesises and cites across your content.
Customer agent
Guides customers through real, multi-step journeys.
Reliability is engineered, not hoped for.
Anyone can demo an agent. Making one reliable enough to act on your systems is the engineering — and it is most of the work. Guardrails, evaluation and observability are not add-ons; they are the product.
Scoped tool permissions
Least-privilege, allow-listed, sandboxed actions.
Human-in-the-loop
Approvals on anything consequential or irreversible.
Deterministic fallbacks
Safe defaults when the model is uncertain.
Evaluation & regression
Task evals and suites that catch drift before users do.
Tracing & observability
Every plan, tool call and outcome, logged and searchable.
Cost & latency budgets
Explicit SLOs, model routing and caching.
Questions a CTO asks.
How do you stop an agent doing the wrong thing?+
Three layers: narrow, permissioned tools; human-in-the-loop approval for consequential actions; and deterministic fallbacks. Combined with evaluation and tracing, the agent operates inside a well-defined box.
When should we NOT use an agent?+
When the process is deterministic. If the steps are known, a workflow is cheaper, faster and more reliable than an agent. We use agents for open-ended, judgement-heavy tasks — not to over-engineer a rules engine.
How do you control cost and latency?+
Model routing (small models for easy steps, large for hard ones), caching, bounded tool loops, and explicit token/step budgets. We treat cost and latency as first-class SLOs.
How is tool access kept secure?+
Agents get scoped, least-privilege credentials, allow-listed tools and sandboxed execution. Every action is logged and, where it matters, gated by approval.
How do you evaluate an agent?+
Task-level evals on real scenarios, offline regression suites, and online tracing of every plan, tool call and outcome — so quality is measured, not assumed.