AI Agent & Automation Engineering is the discipline of building autonomous and semi-autonomous systems that execute real work inside enterprise workflows — under production-grade observability, guardrails, and human oversight. Demos are easy. Shipping agents that a regulated business is willing to trust with customer interactions, financial transactions, or compliance actions is not.
What this means
Agents fail in production for predictable reasons: no evaluation harness, no safety rails, no recovery path when tools misfire, no observability into why they made a decision, and no integration discipline with the systems of record they are supposed to act on.
We engineer agents as production software — with the same rigour you would apply to any other system the business depends on. That means version control, continuous evaluation, structured tool contracts, policy-enforced action surfaces, and human-in-the-loop design where the risk profile demands it.
How we deliver
- Workflow decomposition. We map the end-to-end workflow the agent is replacing or augmenting, identify the decision points, and classify each one by risk and reversibility.
- Tool & integration design. We specify the tools the agent can call, the schemas they accept, and the rollback behaviour for any non-idempotent action.
- Evaluation harness. Before any model selection, we build the evaluation set — real tasks, real data, graded rubrics — so we can measure rather than guess.
- Guardrails & policy layer. Structured policy enforcement (NDPA, internal controls, role-based action limits) sits between the model and the tools, not inside the prompt.
- Observability & feedback loops. Every agent action is logged, attributed, and linkable to the inputs and model version that produced it — so incidents are investigable.
- Phased rollout. Shadow mode → supervised mode → autonomous mode, with explicit promotion criteria at each stage.
Who this is for
This work suits organisations where agents will touch customer-facing, revenue-bearing, or compliance-sensitive workflows — and the cost of a wrong action is material. Typical contexts:
- Financial services firms automating claims triage, KYC review, or credit adjudication support.
- Public-sector and regulated enterprises automating case handling, document review, or citizen-facing enquiry response.
- Operations-heavy businesses replacing repetitive back-office workflows without losing audit trails.
Outcomes you can expect
- Production agents with measurable accuracy, latency, and cost envelopes.
- A reproducible evaluation harness the team can extend post-handover.
- Policy and guardrail layers that satisfy internal risk and external regulators.
- Observability that turns "the agent did something weird" into a specific, debuggable event.
Frequently asked questions
What foundation models do you work with?
We are deliberately model-agnostic. We work across Claude, GPT, Gemini, and open-weight models (Llama, Mistral, Qwen) depending on the use case, data-residency requirement, and cost envelope. Model choice falls out of the evaluation harness, not the sales pitch.
How do you handle data residency and sovereignty?
Where residency is a requirement — NDPA, sector regulation, or client policy — we design for it from the start: in-region inference, BYOK key management, VPC-isolated deployments, or on-prem model hosting as needed.
What agent frameworks do you use?
We select per engagement. We have shipped on LangGraph, CrewAI, custom orchestration, and provider-native agent APIs. Framework choice is an implementation detail — the architecture discipline is what carries across.
How do you prevent hallucinations in production agents?
Hallucinations are a design problem, not just a model problem. We constrain the surface: structured tool outputs, retrieval-grounded generation where factual accuracy matters, policy checks on every external action, and evaluation gates before promotion.
Do you provide post-deployment monitoring?
Yes. Observability is part of the build, not an add-on. Clients receive dashboards covering accuracy drift, cost, latency, policy violations, and human-override rates. Ongoing managed operations is available as a separate engagement.
How long does it take to get an agent into production?
A focused agent on a well-scoped workflow typically moves from kickoff to supervised production in 10–16 weeks. Broader multi-agent systems or heavily regulated environments take longer — usually because the policy and integration surface, not the model work, is what governs the timeline.