Agentic AI & Automation
Outcomes
Work that used to queue gets done continuously
Agents do not batch. A ticket classified and routed the moment it arrives, an invoice reconciled at 2am, a report compiled before anyone asks for it. The gain is often cycle time rather than headcount.
Your people move up a level
The realistic pattern is not replacement, it is the agent handling the routine majority while humans take exceptions and judgement calls. Teams typically report the job getting more interesting, and that matters for whether the deployment survives contact with the people using it.
Consistency you can audit
A well-built agent applies the same policy every time and logs why it did what it did. That trail is frequently more valuable than the automation itself, particularly in regulated environments.
Honest limits, stated up front
Agents are strong where work is repetitive, rule-adjacent and text-heavy. They are weak where the process is undocumented, exceptions outnumber the norm, or a wrong answer is expensive and hard to detect. We tell you which category you are in during scoping, not after invoicing.
What we build
Scoped workflow agents. An agent with a defined job, a defined set of tools, and defined stopping conditions. Not a general assistant. Narrow scope is the single strongest predictor of whether an agent works in production.
Tool and system integration. The agent acts through your actual systems — CRM, ERP, ticketing, database, internal APIs. We build the tool layer, handle authentication and permissions, and constrain what the agent can touch. Write access is granted deliberately, per action, never wholesale.
Human-in-the-loop design. Where a decision carries cost or risk, the agent proposes and a human confirms. We design the approval surface so confirming is fast, because an approval step people find tedious gets clicked through blindly, which is worse than no approval step.
Evaluation harness. A test set drawn from your real historical cases, with measured accuracy before launch and continuously after. This is the part most agent projects skip and the reason most of them cannot answer "is it working?" six months later.
Observability and cost control. Every run traced, token spend attributed per workflow, alerting on failure and drift. Agents can fail quietly — looping, degrading, or becoming expensive — and unmonitored agents are the ones that get switched off.
Fallback and escalation paths. What happens when the agent is uncertain, a tool is down, or the input is malformed. Defined behaviour, not undefined behaviour.
How it works
Weeks 1–2 — Workflow mapping. We sit with the people who do the work today and document how it actually runs, including the exceptions nobody wrote down. We identify decision points, required data, and the boundary of what the agent should be allowed to decide.
Weeks 2–3 — Baseline and test set. We assemble a set of real historical cases with known correct outcomes. This becomes the measure of whether the agent works. Building it before building the agent is what keeps the project honest.
Weeks 3–6 — Build. Agent logic, tool integrations, guardrails, approval surface. Evaluated continuously against the test set. Most workflows reach a usable state faster than expected and then spend the majority of the time on exception handling, which is where the real work is.
Weeks 6–8 — Shadow mode. The agent runs against live traffic without acting. Its proposed actions are compared to what humans actually did. Disagreements are examined. This step is skippable and we recommend against skipping it.
Weeks 8–10 — Staged rollout. Live on a subset with approval gates, widening as accuracy holds. Gates loosen where the numbers justify it, not on a schedule.
Ongoing — Run. Monitoring, evaluation against fresh cases, adjustment as your processes change. An agent is a system that needs an owner, not a project that ends.
Ten weeks is typical for a first production agent. Simpler workflows land in six. Anyone promising a production agent in two weeks is describing a demo.
Technology
Orchestration: LangGraph, the Claude and OpenAI agent SDKs, and custom orchestration where a framework adds more complexity than it removes — which is often, and we will say so.
Models: Claude, GPT and Gemini for reasoning-heavy steps; smaller and open-weight models for classification and extraction where they perform comparably at a fraction of the cost. Most production agents are a mix, routed by step rather than one model doing everything.
Integration: your existing APIs, plus Temporal or similar for durable execution where a workflow spans hours or must survive failure mid-run.
Evaluation: LLM-as-judge for subjective steps, deterministic assertions for everything that can be checked exactly. Braintrust, LangSmith, or a purpose-built harness.
Deployment: your cloud, your VPC, your data residency requirements.
Where this applies
Strongest in operations-heavy functions with high volume and documented-enough process.
Weakest where volume is low, the process changes constantly, or a single wrong action is expensive and hard to reverse. In those cases a simpler assistive tool usually beats an agent, and costs a tenth as much.
What it costs
We scope one workflow at a time and price it fixed. Multi-workflow programmes are sequenced, not bundled, because the second agent is materially cheaper than the first and you should get that benefit rather than pay a blended rate.
Frequently asked questions
Traditional automation follows a fixed script and fails when the input deviates. An agent interprets context and decides among available actions, so it handles variation — unstructured text, ambiguous cases, missing fields. If your process is fully deterministic, conventional automation is cheaper and more reliable. Use an agent where the variation is the problem.
It depends entirely on the workflow, which is why we build the test set before the agent. Well-scoped classification and extraction tasks commonly exceed human consistency. Open-ended multi-step reasoning is less reliable and we design approval gates accordingly. Any vendor quoting an accuracy figure before seeing your data is guessing.
Defined by design: approval gates on consequential actions, confidence thresholds that route uncertain cases to a human, full action logging, and reversibility where the underlying system allows it. We design failure behaviour explicitly rather than hoping it does not occur.
The common outcome is the agent absorbing routine volume while the team handles exceptions and grows into higher-value work. We will not tell you a headcount number, because the honest answer depends on decisions you make, not on the technology.
Yes, with deliberately scoped permissions. Read access is straightforward. Write access is granted per specific action, logged, and typically gated behind approval initially. We do not deploy agents with broad write credentials.
Inference cost scales with volume and model choice, and is usually a small fraction of the labour it displaces — but it is not zero and it can drift. We model it at scoping, instrument it in production, and route cheaper models to steps that do not need frontier reasoning.
Usually, somewhat. Processes built around human judgement often have implicit steps that need to become explicit. This is frequently the most valuable part of the engagement, and occasionally the discovery that the process should be fixed before it is automated.
Depends on workflow volume and current cost. We model this during scoping with your numbers and tell you if the payback period does not justify the build.
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