AI agent development that gets the work done.
An agent earns its place by finishing the task, not describing it. We build autonomous AI agents that plan, use your tools, and complete multi-step work, with human approval where it counts, guardrails you set, and evals that prove they hold up in production.
The difference is autonomy with limits
An agent that acts on your systems, inside boundaries you set
A chatbot answers. An agent does. Given a goal, it plans the steps, calls your tools to look things up and take action, checks the result, and continues until the task is complete. That autonomy is the point, and it's also the risk if it's built without care.
We build agents that are autonomous where it's safe and supervised where it matters. Least-privilege access, approval checkpoints, spend caps, and full tracing mean the agent can only do what you've permitted, and you can always see exactly what it did.
What you get
An agent built to complete tasks, not just start them
Planning, tool use, and memory, wrapped in the guardrails and observability that make autonomy safe to run.
Planning and orchestration
The agent breaks a goal into steps and decides the order to run them, so it completes real tasks instead of answering a single prompt.
Tool use and integrations
Typed connections to your APIs, databases, and SaaS tools, so the agent can look things up and take action in the systems you already run.
Human-in-the-loop approvals
Checkpoints where a person reviews or signs off before anything consequential happens, so autonomy never means losing control.
Guardrails and permissions
Least-privilege access, allowed-action limits, and spend caps, so the agent can only do what you've explicitly permitted.
Memory and state
Durable state across steps and sessions, so a long task survives retries and picks up where it left off rather than starting over.
Observability and tracing
Every decision, tool call, and result logged, so you can see exactly what the agent did and why when you need to audit a run.
Evaluated reliability
Test suites over real tasks that measure success rate and catch regressions, so you ship on evidence, not a demo that worked once.
Owned, production-grade code
Typed, tested, documented code you own and can extend, so you're never locked to a black-box platform or a single vendor.
How we build it
From a scoped task to an agent you can trust in production
A clear path from defining the job to a deployed, evaluated agent, reviewed at every stage.
- 01
Discovery & task scope
We map the task the agent should own, where a human must stay in the loop, and how we'll measure that it succeeds.
- 02
Tools & permissions
We design the tools the agent can call and the exact permissions and limits around each, before it touches production.
- 03
Build the agent loop
We build the plan-act-observe loop, tool integrations, memory, and approvals, typed and tested, shipped in reviewable iterations.
- 04
Evaluate & harden
We run evals on real tasks, add recovery for failures, and tighten guardrails, so the agent behaves under messy inputs.
- 05
Deploy & monitor
We deploy with tracing and alerts, watch live runs, and tune from what they reveal, so reliability improves with use.
- Python
- TypeScript
- Anthropic Claude
- OpenAI
- LangGraph
- Temporal
Why it matters
Agents scale the work people shouldn't be doing by hand
A lot of expensive work is repetitive coordination: pulling data from several tools, following a runbook, and acting on the result. An agent that owns that end to end frees your team for the judgment calls, and it works at a scale and speed that manual effort can't match.
The risk is an agent that acts on the wrong thing, or acts confidently when it shouldn't act at all. Scoped permissions, human approvals, and evaluated reliability are what keep autonomy on the right side of that line, and because you own the code, you can widen what the agent handles as your trust in it grows.
Related services
Keep exploring
AI Development
The full category: LLM apps, agents, chatbots, and automation, in production.
Learn moreAI Chatbot Development
When you need to answer questions in conversation, not run multi-step tasks.
Learn moreAI Automation
Automations that use AI on language-heavy work along a defined path.
Learn moreAI Application Development
The full AI-powered product an agent often lives inside.
Learn more
FAQ
AI agent development, answered
Thinking about an agent? Book a call and we'll scope it with you.
What is an AI agent, and how is it different from a chatbot?
A chatbot answers questions in a conversation and can do light lookups. An AI agent goes further: it plans a task, uses your tools to act, checks the result, and keeps going until the job is done, with more autonomy across multiple steps. If you mainly need answers, a chatbot fits; if you need work actually completed across your systems, that's agent development.
What kinds of tasks can an AI agent handle?
Multi-step knowledge work that spans tools: triaging and resolving support tickets, researching and drafting from several sources, reconciling data across systems, running parts of an onboarding or fulfilment flow, or monitoring for a condition and acting on it. The best fit is a task with a clear goal, defined tools, and a way to check the result.
How do you keep an autonomous agent under control?
We give it least-privilege access, limit it to an explicit set of allowed actions, and put human approval checkpoints before anything consequential runs. We add spend and rate caps, and log every step so a run can be audited. Autonomy is scoped on purpose, so the agent can act freely only inside the boundaries you set.
How do you stop agents from going off track or failing silently?
We build evaluation suites over real tasks to measure success rate and catch regressions before release, and add recovery so a failed step retries or escalates instead of compounding. In production, tracing and alerts surface problems early, and durable state means a long task resumes rather than restarts after an error.
Which models and tools do you build agents with?
We're model-agnostic and choose per case, working with providers like Anthropic Claude and OpenAI, and open models where privacy calls for it. For orchestration and durability we use frameworks such as LangGraph and Temporal. Your data is used to run the task, not to train models without your explicit agreement.
How long does an AI agent engagement take?
It depends on how many tools the agent touches and how high the stakes of its actions are. A focused agent against a couple of systems can reach a useful pilot in a few weeks; broader, higher-risk automation takes longer because it needs more evaluation and tighter guardrails. We scope it with you on a call and ship in iterations you can review.
Let's build something worth shipping
Tell us what you're working on. Book a call to talk through it, or send a few details and we'll get back to you with next steps.