AI Agent Development
AI agents that do the work, not just the talking.
Anyone can wire an LLM to an API and call it an agent. Making one that plans, uses tools, recovers from failure and knows when to stop is engineering. It's the hardest thing we do, and the thing we're best at.
What we build
Tool-using agents
Agents that query your systems, take permissioned actions and complete multi-step tasks. Every tool is schema-validated, scoped and logged.
Agent harnesses & orchestration
The machinery around the model: planning loops, checkpointed state, retries with strategy changes, and budgets. It's the pattern behind systems like Claude Code, applied to your domain.
Multi-agent systems
Specialist agents that decompose, parallelize and verify each other's work, coordinated by deterministic orchestration rather than hope.
Eval suites & guardrails
Task-level evals that gate every prompt and model change, plus input and output guardrails for injection, abuse and policy compliance.
Why agents fail, and how we build them so they don't
State lives outside the prompt
Agents that accumulate everything into one growing prompt get slower, costlier and dumber. We design explicit state: what the agent knows, what it has done, what remains. Long runs survive restarts because state is checkpointed.
Tools are a security boundary
An agent is only as safe as its least-considered tool. Every tool we ship is typed, permission-scoped, rate-limited and audited. The agent can only touch what you decided it can touch.
Evals before features
Before an agent gets a new capability, it gets a test suite for that capability: real tasks with verifiable outcomes. That's how you change prompts and models without praying.
Failure is a designed path
Timeouts, malformed tool output, model refusals, rate limits. Production agents hit all of them. Ours retry with different strategies, degrade gracefully and escalate to humans with full context.
Where AI agents earn their keep
- Support operations: agents that resolve tickets end-to-end instead of drafting replies
- Back-office workflows: document intake, reconciliation, compliance checks
- Engineering productivity: code-review, migration and test-generation agents
- Research and analysis: multi-source investigation with cited, verifiable output
- Sales operations: lead research, enrichment and qualification at scale
FAQ
Questions we hear a lot
What is an AI agent, in plain terms?
An AI agent is software where a language model doesn't just answer questions. It pursues a goal: it plans a task, uses tools such as APIs, databases and documents, checks its own progress and keeps going until the job is done or a human needs to step in. Think of the difference between asking someone for directions and hiring a driver.
How long does an AI agent development project take?
A focused pilot covering one workflow with real data and a measurable outcome typically takes 4 to 8 weeks. Production hardening with evals, guardrails, monitoring and handoff flows usually adds another 4 to 6 weeks depending on integration depth. We scope honestly during discovery, before you commit.
Which models do you build on?
We're model-agnostic: Claude, GPT, Gemini, DeepSeek and open-weight models like Llama and Kimi, chosen per project for quality, latency, cost and data-residency constraints. Because we build eval suites first, we can swap models later and prove nothing broke.
How do you keep an agent from doing something it shouldn't?
Layers. Permission-scoped tools mean the agent physically can't touch what it wasn't given. Input classification defends against prompt injection. Output checks enforce policy. Human approval gates protect irreversible actions, and full audit logs record every step. Safety is architecture, not a system prompt.
What does it cost to run an AI agent in production?
Far less than most teams assume when it's engineered properly: right-sized models per step, caching, and token budgets per run. We give you a cost model during design covering cost per task and per month at your projected volume, so there are no surprises.
Can you work with our in-house engineering team?
Yes, and we prefer it. We can deliver turnkey, embed with your team, or design the architecture and review your implementation. Every engagement includes documentation and handover. We don't build black boxes you depend on us to operate.
From our writing
What Makes an AI System 'Agentic'? A Plain-English Guide for Decision-Makers
Agentic AI is the difference between software that answers and software that acts. Here's what the term actually means, why agents are hard to build, and how to tell whether your business needs one.
AI Agent Development Cost in 2026: What You'll Actually Pay
A breakdown of AI agent development cost in 2026, from proof of concept to multi-agent enterprise systems, plus the ongoing costs teams forget to budget.
AI Agent vs Chatbot: Which One Does Your Business Actually Need?
AI agent vs chatbot, explained as a practical buying decision: what each one actually does, where the real line sits, and how to pick without overspending.
Why AI Pilots Fail to Reach Production (and How to Design One That Won't)
Most corporate AI pilots die between the demo and deployment. The causes are predictable and mostly avoidable. Here are the five failure modes we see, and a pilot design that dodges them.
Ready when you are.
Tell us what you're working on. You'll get an honest read on feasibility, timeline and cost.