Service
AI agents that invoke your product APIs
Server-side orchestration for multi-step workflows — tool definitions scoped to your auth model, with audit trails and confirmation gates before irreversible actions.
Who this is for
Teams moving beyond single-turn copilots to workflows where the model selects actions — update records, query live data, trigger internal processes — and those actions must respect the same RBAC and compliance bar as the rest of the product.
Problems we solve
Common failure modes when copilot, retrieval, or middleware features are bolted on without an integration plan.
- Agent POCs with broad database access or raw internal endpoints exposed to the model runtime
- No structured logging of which tools ran, with what arguments, and whether the user confirmed
- Framework demos that work in a notebook but bypass your session auth, rate limits, and tenant isolation
Typical deliverables
- Tool surface design — narrow, typed invocations of your existing APIs with explicit permission checks per action
- Orchestration layer in your repo — step routing, retries, timeouts, and human-in-the-loop confirmation for destructive operations
- Integration through LLM middleware — every agent run logged, traced, and bounded by per-tenant cost and rate policies
- Eval harness for tool selection and outcomes — regression gates before prompt or tool schema changes ship to production
How we deliver
Your eng team stays on the roadmap. We handle the AI integration layer — scoped sprints, PRs to your repo, and handoff docs so your team can operate what we ship.
We scope agents to a single workflow boundary first — one job the user already does manually that benefits from orchestration. The audit maps your API surface, auth model, and which actions require confirmation. A prototype validates tool calls against your real stack before expanding step count or tenant rollout. Framework choice (LangChain, custom loop, or lighter orchestration) follows your team's constraints — production security lives in the tool implementations, not the library.
Step 1
Technical audit
Map your architecture, API boundaries, data flows, and auth model. Identify the lowest-risk, highest-value integration point.
Step 2
Architecture & prototype
API contracts, middleware design, and a working proof against your real stack — validated before full build commitment.
Step 3
Build & deploy
Production code in your repo. Staging, load testing, and canary rollout behind feature flags — with runbooks for your team.
Step 4
Operate & expand
Monitor latency, cost, and output quality. Iterate on evals and prompts, then extend to the next workflow boundary.
Related guides
Deeper technical notes from our resources library.
Build an agent with LangChain — a practical tutorial
Step-by-step guide to building a tool-calling agent with LangChain and LangGraph, from first prototype to patterns that survive production.
June 6, 2026
Prompt injection and LLM security for SaaS
A practical security guide for multi-tenant products — why system prompts are not enough, where attacks actually land, and the integration patterns that hold up in production.
June 9, 2026
LLM middleware: what it is, why you need it, and how to implement it
A practical guide to the server-side layer between your app and the model — auth, rate limits, routing, logging, and the patterns that keep AI features production-ready.
June 7, 2026
Common questions
- Do we need LangChain or a specific agent framework?
- No. We choose orchestration based on your stack and team — a thin custom loop, LangChain, or another library — and implement tools against your existing APIs. The integration layer (auth, logging, confirmation gates, evals) is what makes agents production-ready, not the framework brand.
- How do agents differ from an in-app copilot?
- Copilots often assist within a single view with context already on screen. Agents orchestrate multi-step workflows — selecting tools, calling APIs in sequence, and handling intermediate results. Many products start with a copilot and add agent capabilities once middleware and tool boundaries are in place.
- Tools call your product APIs through the same authorization layer as the rest of your app — never raw credentials or broad internal endpoints. Sensitive actions require explicit user confirmation, and every invocation is audit-logged with tenant and actor context. See our guide on prompt injection and LLM security for the full pattern.
Scope an integration for your stack
Describe the feature you are planning — we will map architecture, effort, rollout strategy, and what production-ready means for your system.
Get an integration plan