AI Integration · Custom Agents
Production-grade AI agents — built, not glued together.
When an off-the-shelf integration doesn’t fit, we build the agent. Tool-using, memory-aware, observable, cost-governed. Designed around your domain, your data, and the failure modes that matter to your business.
What “production-grade” actually means
Most agent demos are a Jupyter notebook and a clever prompt. They’re convincing on stage and unsuitable for anything your customers or operations team will rely on. Production-grade is different work: durable execution so a transient API failure doesn’t lose state, observability so you can debug what the agent did at 3am, guardrails so an off-script action gets caught, cost governance so the bill is predictable, and version control so a prompt change rolls out the way code does.
That’s the gap between “we wired up an LLM” and “we shipped an agent we’d trust with our own business”. We build for the second one.
The architecture we build to
Tools, not just text
A useful agent acts. We define a typed tool surface — query your database, call your API, post to Slack, file a ticket, send an email, fetch a document — and the agent picks the right one for the situation. Every tool call is validated against a schema, executed with the right permissions, and logged.
Memory that’s deliberate
Short-term context for the active task. Long-term memory in a retrieval layer over your domain knowledge. Per-user or per-account memory where it makes sense. We don’t stuff the whole world into the prompt window — that’s how cost runs away and accuracy drops.
Observability built in from day one
Every reasoning step, every tool call, every retrieval is traced. We instrument with Langfuse, LangSmith, OpenTelemetry, or a custom logging layer depending on stack. When the agent does something weird, you have the full trace — not just the final output — to figure out why.
Guardrails as code
Input filters for prompt-injection and PII leakage. Output filters for forbidden actions, off-brand language, and policy violations. Hard-coded action limits — the agent literally cannot spend more than $X, cannot send more than N messages per hour, cannot touch the production database in write mode without explicit human approval. Guardrails are configuration, not hope.
Predictable infrastructure
Every agent we ship comes with durable execution, full tracing, hard cost caps, and a reversible action log. That’s the baseline — not the upsell.
The frameworks and models we use
We’re framework-pragmatic — we pick what fits the problem, not what’s loudest. In active production: the Anthropic Claude SDK and Claude Agent SDK, OpenAI’s Assistants and Responses API, LangChain / LangGraph where its graph model fits, LlamaIndex for retrieval-heavy work, and Temporal or AWS Step Functions for durable orchestration of long-running agents. For UI, we build into your existing app — React, Vue, or whatever your team already ships.
Choosing the right model
Model choice is a cost-and-latency decision as much as a quality one. We don’t put a frontier model on every step.
- Frontier models (Claude Opus, GPT, Gemini Pro tier): Hard reasoning, ambiguous classification, planning steps in multi-step agents.
- Mid-tier (Claude Sonnet, GPT mid-tier): The workhorse for most tool-using agents — fast enough, smart enough, priced reasonably.
- Small / fast models (Claude Haiku, smaller GPTs, open-source): Routing, simple extraction, validation passes, anything called in a tight loop.
- Open-source self-hosted (Llama, Mistral, Qwen): When data sensitivity or volume demands it.
The same agent will often route between three tiers depending on the step. That’s where the real cost savings live.
Where the agent runs
Three deployment patterns. Picked by what the agent touches:
- Vendor API (default): Claude, GPT, or Gemini via no-retention endpoints. Lowest infra overhead, fastest to ship.
- Your cloud: The agent runtime, orchestration, and state run inside your AWS, Azure, or GCP tenant. Vendor models accessed via your own keys.
- On-prem / open-source: Llama, Mistral, or Qwen running on your own hardware. For regulated, privileged, or air-gapped use cases.
How we control AI cost
Agents are the most expensive AI category to deploy poorly — recursive tool calls, runaway reasoning loops, and bloated context windows can multiply your bill in a single buggy release. Every agent we ship has:
- Hard per-turn and per-session token caps
- Tiered model routing as described above — frontier models only where they earn it
- Prompt caching for stable system prompts and reusable context
- Step budgets — the agent literally cannot take more than N tool calls or more than X dollars per task
- Daily and monthly spend ceilings with automatic shutoff and alerting
- A real-time cost dashboard broken down by agent, by user, by task
When a custom agent is the right answer
Most of the time it isn’t — an integration into your CRM, helpdesk, ESP, or document system will move the needle faster and cheaper. Custom agents earn their place when:
- The workflow is domain-specific enough that no integration covers it
- The task requires multi-step reasoning across several tools and systems
- You want a user-facing AI experience inside your own product
- The data sensitivity or competitive value of the workflow makes a vendor integration the wrong fit
We’ll tell you on day one which bucket you’re in. If a custom agent isn’t the answer, we’ll point you back to the integration that is.
The 30-day proof of value
Pick one well-scoped agent task — a research assistant for your sales team, an onboarding agent inside your product, an operations agent for a specific internal workflow. We’ll ship the production-grade version in 30 days, with observability, guardrails, and cost caps live from day one. Measured against an acceptance criterion we agree before we start. If it doesn’t hit, you don’t scale. Cost ceiling in writing on day one.
Frequently asked
Do you build agents that act autonomously, or do humans approve every action?
Depends on the action. Read-only and low-risk write actions can run autonomously. Anything that touches money, customers, or production data goes through a human approval step by default. You can move the threshold as your confidence in the agent grows. We don’t ship full-autonomy agents into customer-touching surfaces on day one.
Will the agent work with our existing API and database?
Yes. We build typed tool wrappers around whatever your services already expose. If you have an OpenAPI spec, GraphQL schema, or a documented internal API, we use that. If you don’t, we’ll write tooling that respects your existing access controls — the agent gets the same permissions as a human user would.
How do you handle prompt updates and model upgrades?
Prompts are versioned alongside code. Model upgrades go through an eval suite we build with you on day one — same inputs, regression-tested outputs — before they reach production. Rollback is one config change. No silent model swaps.
What does this cost ongoing?
Two components: your token spend (depends entirely on volume and how aggressively we tier — anywhere from a few hundred dollars a month for an internal agent to mid-five-figures for a customer-facing one at scale) and an optional monthly retainer for monitoring, eval maintenance, and prompt updates. Both sized honestly with a written ceiling before you commit.