AI Agent Development
We design and ship autonomous AI agents that take real actions — with tools, memory, guardrails, and a human in the loop exactly where it matters.
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What is an AI agent?
An AI agent is a system that can reason about a goal, choose from a set of tools, and take multi-step actions to accomplish it — not just answer a single prompt. Where a chatbot responds, an agent does: it queries your data, calls your APIs, updates records, and knows when to ask a human.
Done well, agents remove entire categories of repetitive work. Done carelessly, they take wrong actions at scale. Our practice is built around the second problem: every agent we ship is scoped, observable, evaluated, and reversible.
Where agents earn their keep
Customer support
Resolve tier-1 tickets end to end, with grounded answers and clean handoff to human agents.
Sales & CRM ops
Enrich leads, log activity, and draft personalized follow-ups directly inside your CRM.
Internal research
Agents that search, synthesize, and cite across your knowledge base on demand.
Data operations
Reconcile, clean, and route records across systems — no more manual spreadsheets.
Onboarding & IT
Provision access, answer policy questions, and complete checklists autonomously.
Finance & billing
Categorize transactions, flag anomalies, and prepare reports with a full audit trail.
What every agent ships with
No agent leaves our shop as a black box. Each one arrives with the same production-grade scaffolding — so it’s safe to trust with real work.
Tool use
Scoped access to your APIs, databases, and internal tools — with permissions defined per action.
Grounded memory
Retrieval over your knowledge base so answers cite real sources, plus session memory that persists context.
Guardrails
Explicit boundaries on what the agent can and cannot do, with fallbacks when confidence drops.
Human in the loop
Clean escalation paths so a person approves or takes over exactly where judgment is required.
Evaluation suites
Automated tests that catch regressions before they reach users, run on every change.
Full observability
Every action logged, traced, and reversible — so you can audit, debug, and roll back with confidence.
How we build an agent
Evaluate the fit
We test the task on real data against a clear success bar before committing to a build — proving the agent earns its place.
Scope tools & guardrails
Define exactly what the agent can access and do, with permissions, fallbacks, and escalation rules.
Build & ground
Wire the agent into your data and APIs with retrieval, memory, and orchestration that survives model swaps.
Evaluate & harden
Automated eval suites and observability catch regressions before they reach users.
Ship & improve
Launch with monitoring, then tune prompts, tools, and models as real usage data comes in.
Agent development FAQ
A chatbot returns text. An agent reasons about a goal and takes actions across tools and APIs — retrieving data, updating records, and escalating to a human when confidence is low.
The tools we build with
The orchestration and eval layers stay fixed while models move — so swapping a model is a config change, not a rebuild.
Models
Anthropic
OpenAI
Mistral
OllamaAgent runtime
LangGraph
LangChain
Temporal
FastAPIMemory & retrieval
pgvector
QdrantPostgreSQL
RedisEvals & observability
Weights & Biases
OpenTelemetry
Grafana
SentryRelated work
Related reading
Ready to put an agent to work?
Tell us the workflow. We'll come back with a scoped agent, guardrails, and a success bar.