Agentic AI Backend Development
Most AI features fail at the same place. The model is fine. What is missing is the backend around it — the part that decides which tools an agent may call, what context it is given, what happens when a tool errors, and how anyone finds out that the answers have quietly degraded.
That backend is ordinary distributed systems engineering with an unusual failure mode: it fails plausibly. A broken API returns a 500 you can alert on. A poorly grounded agent returns a confident paragraph that reads correctly and is wrong. We build the layer that makes that difference visible.
What we build
Tool and function calling
Typed tool definitions, argument validation, permission scoping, and deterministic handling of malformed or repeated calls. Agents get a narrow, auditable surface rather than open access to your systems.
Retrieval pipelines
Chunking, embedding and index selection driven by how the content is actually queried, plus hybrid keyword and vector retrieval, re-ranking, and honest handling of the case where nothing relevant is found.
Orchestration and state
Multi-step workflows with durable state, retries, timeouts and cancellation. Long-running agent runs survive process restarts instead of losing their work halfway through.
Evaluation harnesses
Regression suites over a fixed set of cases so a prompt or model change is measured rather than guessed at. Scores tracked over time, run in CI, with failures blocking the deploy.
Guardrails and safety
Input and output validation, prompt-injection defences on retrieved content, refusal handling, PII redaction, and hard limits on cost and step count per run.
Observability and cost control
Per-run tracing across every model and tool call, token and latency accounting per feature, and budget alerting — so cost is attributable before the invoice, not after.
Grounding is an engineering problem
Retrieval quality sets the ceiling on everything downstream. No amount of prompt work rescues an agent that was handed the wrong three documents, and the failure is silent because the model will answer anyway.
We treat retrieval as a system to be measured: a labelled set of questions with known correct sources, recall tracked at fixed cut-offs, and re-ranking introduced only where it demonstrably earns its latency. When retrieval returns nothing useful, the correct behaviour is to say so rather than to improvise.
Model choice belongs behind an interface
Providers change pricing, deprecate versions, and ship new models on their own schedule. Code that calls one vendor's SDK directly from a request handler inherits all of that.
We put model access behind an internal interface with routing, fallback and per-feature configuration, so a model swap is a config change measured against the evaluation suite rather than a refactor. Cheaper models handle the routine paths; the expensive ones are reserved for the work that actually needs them.
Working with what you already run
Agentic features almost never arrive on a blank page. There is an existing database, an authentication model, a permissions system and a compliance position, and the AI layer has to respect all of it.
In practice this means the agent's tools enforce the same authorisation as the rest of your API, retrieval respects per-tenant boundaries, and nothing reaches a third-party model that your data policy does not allow to leave. These constraints are set at the start, not retrofitted.
Frequently asked questions
Do you train custom models?
Rarely, and only when there is a clear case for it. Most problems presented as needing a fine-tuned model are retrieval, prompt or evaluation problems, and are cheaper and faster to solve at that level. We will tell you which one you have before any training budget is committed.
Which model providers do you work with?
We are provider-agnostic and build behind an internal abstraction. We commonly work with Anthropic's Claude models, OpenAI and open-weight models running on your own infrastructure, and will recommend based on your latency, cost and data-residency constraints.
Can the AI layer run without sending data to a third party?
Yes. Where data residency or policy requires it, we deploy open-weight models into your own cloud or on-premise environment. The trade-offs in capability and operating cost are laid out honestly before the decision is made.
How do you stop an agent doing something harmful?
Through scope rather than instruction. Agents get narrowly defined tools with validated arguments and the same authorisation checks as any other client, plus hard limits on steps and spend. Prompt-level instructions are a last line, not the primary control.
Tell us what you are building.
Every engagement starts with a technical conversation, not a quote. Tell us where the system is today and we will tell you honestly what we think it needs.