Custom AI Agent Development

Beyond chatbots: agents that plan, call tools, query your systems, and complete real work — built with guardrails, memory, evaluation harnesses, and observability.

Get Started All AI Services

Custom AI Agents

AI agents that act — safely and at production scale.

Production-grade agents tailored to your domain, your tools, and your risk tolerance.

Common signs your team is overdue for custom ai agents:

  • Generic agents don’t know your business rules, data, or systems
  • No observability — you can’t tell why an agent did what it did
  • No evaluation harness — “improvements” are a coin flip
  • No guardrails — agents take destructive actions you didn’t expect

What we build for custom ai agents:

  • Planner + executor architecture with explicit tool schemas
  • Short-term and long-term memory (episodic + semantic)
  • Guardrails: scoped tool access, cost ceilings, action approvals
  • Eval harness with task-level scoring and regression tests
  • Tracing & observability — every step inspectable
Talk to an engineer

Capabilities

Agents that earn their keep

Production-grade agents — outcomes our clients keep coming back for.

Research agent

Browses sources, extracts evidence, writes citations-attached briefs.

Ops agent

Watches alerts, runs runbooks, opens tickets with proposed fixes.

Analyst agent

Queries your data warehouse, builds charts, writes the narrative.

Triage agent

Classifies incoming requests, gathers context, routes with a recommendation.

How we deliver

Agent-build sprint

01

Scope the job

Define the exact task, the tools the agent may use, and what success looks like.

02

Build the eval first

Tests come before the agent. We can’t improve what we can’t measure.

03

Implement & instrument

Build the agent with tracing on day one. Every decision is inspectable.

04

Ship & monitor

Deploy with dashboards, alerts, and an on-call rotation if needed.

Tools & platforms we use:

OpenAI Anthropic LangChain LangGraph LlamaIndex FastAPI Postgres Redis Langfuse Temporal Kubernetes

FAQ

Questions teams ask us about Custom AI Agents

How do you stop the agent doing something it shouldn’t?
Agents only have access to tools you grant. Sensitive actions require explicit approval. We add cost ceilings, rate limits, and a kill switch by default.
When is an agent overkill?
Often. If a deterministic workflow with one or two LLM steps gets you 95% of the way there, that’s what we build. Agents earn their complexity.
How long does it take to get to production?
Most projects ship a real, usable system in 3–6 weeks. Discovery is 1–2 weeks; build sprints are weekly with demos.
Will my data be used to train models?
No. We default to enterprise tiers (OpenAI, Anthropic, Bedrock, Vertex) that don’t train on your data. For sensitive use cases, we deploy open-weight models on your infrastructure.
How do you control costs?
We design cost-aware from day one — model routing (cheap model first, escalate when needed), caching, batch processing, and per-user budgets with alerts.
Can you work with our existing engineering team?
Yes. We embed alongside your team, transfer ownership progressively, and document everything we build.