The letters AI rendered in blue over a network of connected nodes

Agentic workflow engineering

We design multi-step AI workflows that understand context, choose the right tools, act across systems and pause when a person should decide. Every agent ships with permissions, structured outputs, evaluations, retry paths and observability. Model chosen per job — Claude, Azure OpenAI or open weights — never by default, and always on your accounts.

What We Do
We design multi-step AI workflows that understand context,…
In this section

What it covers

  • Planning and orchestration — Multi-step plans, tool selection and hand-offs between specialised agents, with a deterministic controller deciding what runs next.
  • Context, memory and retrieval — Retrieval-augmented generation over your documents with citations, per-tenant memory, and refusal when nothing relevant is found.
  • Tool calling and human approval — Typed tool contracts, permission scopes per action, and approval gates where money, data or customers are affected.
  • Durable execution and recovery — Long-running jobs that survive restarts, idempotent steps, retries with back-off and a dead-letter path someone actually reads.
  • Evals and observability — Fixture transcripts with known triggers, pass/fail logs per prompt version, and cost per operation from the first week.
  • Security and deployment — Prompts assembled server-side so they can't be tampered with; keys, logs and inference in your cloud, in the region your data must stay.

How we work

Deterministic code does the deterministic work — classification, scoring, retrieval, routing — and the model does interpretation and language. Prompts live in version control with tests. We report cost per operation and containment rate from the first week, and every agent action is logged so a person can audit what it did and why.

Proof

Frequently asked questions
  • Which model do you recommend?+

    It depends on the task, the budget and where the data may be processed. We benchmark two or three candidates on your fixtures and show the numbers before choosing.

  • How do you stop hallucinations?+

    Retrieval first, generation second, citations always, and a refusal path when the retrieved context doesn't answer the question. Then we measure it.

  • When should an agent stop and ask a person?+

    Whenever the action is hard to reverse — a payment, a deletion, a message to a customer. We put an approval gate there and log the decision either way.

  • Can this run on our own cloud account?+

    Yes — and it should. Keys, prompts and logs stay in your Anthropic, Azure or AWS accounts.

KineticHex team
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