AI Agents

AI agents, designed for real operational complexity

If you're embedding agents into a product, automating cross-system operations, or running processes where every action has to be auditable, the gap between "the demo worked" and "this runs reliably" is where most projects get stuck. That's the gap we build in — with the observability, cost control, and oversight that production actually requires.

Overview

What are AI agents?

An AI agent is a software system that uses an AI model to plan, decide, and act — calling tools, reading data, and taking real actions across multiple steps to reach a goal. Unlike a chatbot, it works toward an objective. Unlike a workflow, it makes its decisions dynamically.

The category spans a wide range. Simple agents built with no-code tools work for narrow, well-defined tasks. Agentic systems that operate inside real software, take consequential actions across business systems, and run reliably without supervision are a different problem entirely. That's the kind we build.

We design agent systems around three principles:

Bounded autonomy

Agents should have clear boundaries on what they can do, which tools they can call, how long they can run, and how much they can spend. Constraint isn't a limitation — it's what makes agent behaviour predictable enough to deploy.

Observability by default

Every decision an agent makes, every tool it calls, every input and output should be logged and traceable. Without this, debugging an agent in production is close to impossible, and so is improving it over time.

Human review where the stakes are real

Not on every action — which defeats the point of using an agent — but at the steps where the consequences of getting it wrong materially matter. The right place for human-in-the-loop depends on the specific process, and getting that placement right is part of the design work.

Audience

Who we work with

  • Engineering and product leaders considering AI agents as part of a product or internal system, who want a clear view of what reliable agent implementation actually involves.
  • Operations leaders with multi-step, cross-system processes that don't fit standard automation tools — work that requires judgement, context, or adaptation between steps.
  • Teams that have tried off-the-shelf agent frameworks or no-code tools and found the resulting systems don't meet the reliability bar their use case requires.
  • Organisations where every agent action needs to be auditable — traceable and, where appropriate, reversible.
Challenges

The problems we most often see

  • Processes too irregular for standard automation. Workflows that span multiple systems, branch on context, involve approvals, or adapt to exceptions tend to outgrow no-code connectors. These are often good candidates for agents — but only if the agent is built for the specific process, not assembled from generic examples.
  • Prototypes that don't survive contact with production. An agent that works well in a demo can behave unpredictably once it meets real data, real tool responses, real rate limits, and real users. The gap is usually in the parts that don't show up in demos: error handling, retries, state management, cost controls, and evaluation.
  • Capable agents without proper oversight. An agent that can take real actions in real systems needs controls that match what those actions can affect. Audit trails, permissions, and review interfaces work much better when they're built in from the start. Adding them later is painful and brittle.
    • Approach

    How we approach the work

    • 01 — Question whether an agent is the right answer. Some processes that look agent-shaped are better served by deterministic workflows with occasional model calls. Getting this decision right early avoids a lot of work later, and we'd rather lose the engagement honestly than deliver a system that's the wrong shape for the problem.
    • 02 — Design for observability from day one. Every step an agent takes should be inspectable: which tool it called, with what inputs, what it got back, what it decided to do next. Without that visibility, both debugging and improvement become guesswork.
    • 03 — Constrain the action space deliberately. Tool-level permissions, action limits, cost caps, well-defined stopping conditions. Not because we distrust the model, but because constraint is what makes agent behaviour predictable enough to operate at scale.
    • 04 — Place human review where the risk lives. We design human-in-the-loop controls around the actual risk profile of the process, not the convenience of the engineering. The interfaces are designed for the people who'll use them, because review steps that don't fit how reviewers work get bypassed quickly.
    Use Cases

    Where AI agents fit

    Software companies and product teams

    Agents built into your product that help users complete multi-step tasks faster — without needing to click through several screens or systems.

    Common use cases:

    • Research assistants that pull from multiple sources and return a structured answer
    • Onboarding agents that guide users through setup and complete steps for them
    • In-product agents that generate, edit, or transform content using the product's own data
    • Workflow agents that handle complex sequences inside the product on the user's behalf

    Operations teams in mid-sized companies

    Agents that handle multi-step work across your internal systems — the kind that needs judgement between steps, not just data passed from A to B.

    Common use cases:

    • Operations agents that pull data from different tools and prepare reports or summaries
    • Triage agents that sort incoming requests, find the relevant context, and route them
    • Preparation agents for analysts, advisors, or specialists who need information assembled before they review it
    • Process agents for variable, repetitive work that mixes AI judgement with business rules

    Multi-team and compliance-heavy processes

    Agents built for environments where every action needs to be traceable, every decision recorded, and a human in the loop at the steps that matter.

    Common use cases:

    • Compliance agents that draft assessments and pass them to a human for approval
    • Audit-ready workflows where the agent's reasoning is fully inspectable
    • Multi-step approval processes where the agent prepares information for the right reviewers
    • Regulated process automation with role-based controls and exception handling
    Honesty

    When an agent might not be the right answer

    Not every AI problem is an agent problem. We've steered clients away from agents when:

    • A traditional workflow with targeted model calls would be cheaper, faster, and more reliable.
    • The task is high-volume but structurally simple — where a well-built pipeline outperforms an agent on cost and latency.
    • The business can't tolerate the variability that comes with any probabilistic system, even with strong guardrails.

    Part of our job is to say so when it applies.

    Process

    How a typical engagement runs

    Most agent projects move through five phases. We work alongside your team throughout, with weekly check-ins so you can see progress, raise questions early, and shift priorities as the project evolves.

    1

    Discovery

    We start by understanding the process the agent would support — what the goal is, what systems are involved, what the risk profile looks like, and what success would look like. The output is a written plan with a realistic scope, timeline, and cost — including an honest recommendation on whether an agent is the right approach at all.

    2

    Build

    We design and implement the agent alongside your team, including the tool interfaces, permissions, and orchestration logic. Logging, tracing, and cost monitoring are built in from the first commit.

    3

    Validation

    We test the agent against realistic scenarios — not just the happy path — including failure modes, ambiguous inputs, and conditions that stress the agent's decision-making. Where the agent will run autonomously, we test the constraints first.

    4

    Deployment

    Production rollout with monitoring, alerting, cost limits, and the human-in-the-loop controls the use case requires. We stay close during the first weeks of live use, because agents in production tend to encounter situations that no test environment can fully anticipate.

    5

    Handover, and what comes next

    We hand over the agent with full documentation and a clear plan for how your team will own and evolve it. Everything we build — code, infrastructure, operational knowledge — is yours.

    From there, you have two options: take the agent in-house and run it yourselves, or have us continue alongside you for monitoring, issue handling, and ongoing updates as model capabilities evolve. Both work for us; we'll talk through the choice at the start of the engagement.

    Get in touch

    Let's talk about your project

    Most engagements begin with a short discovery phase: a few days spent understanding the process, the systems, the risk profile, and what success would actually look like. The output is a written plan with a realistic scope, timeline, and cost — and an honest read on whether an agent is the right approach for what you're trying to do.

    We're glad to start the conversation, whether you have a clearly scoped project, a rough idea you're still thinking through, or a specific problem you'd like a second opinion on.

    Contact us