AI Agent Development Services

A chatbot answers. An agent acts. A chatbot returns text from a single turn of retrieval; an agent decomposes a goal into steps, calls tools and APIs to execute them, checks its own output, and retries until the goal is met without a human prompting each step. The difference shows up in the failure mode: a bad chatbot gives a wrong answer, a bad agent takes a wrong action. That is why every agent we ship has guardrails and an approval gate on anything irreversible.

Between $8,000 and $150,000+. A single-purpose proof of concept with one data source runs $8,000–$20,000 over two to four weeks. A two-to-four agent workflow wired into your CRM or ERP runs $25,000–$60,000 over six to eight weeks. A multi-agent system in your own VPC with a compliance review runs $70,000–$150,000+ over ten to sixteen weeks. Three things move the number: how many systems the agent must touch, how high the accuracy bar is, and whether it runs on a shared model API or your own infrastructure.

Four to twelve weeks. A single agent with two or three tool integrations reaches production in about four weeks. Multi-agent systems with custom infrastructure take ten to twelve. Either way you see a working demo on your real data by week three before the expensive part starts.

Usually neither. Most agents we build run on a foundation model GPT, Claude, Gemini grounded on your data through retrieval, not training. We fine-tune only when an off-the-shelf model cannot hit your accuracy bar, and we will tell you at the feasibility stage whether that is likely. What you do need is data the agent can reach: an API, a database, or a document store.

LangGraph when the workflow has to be auditable and deterministic. CrewAI when time-to-first-version matters more than tight control. AutoGen when the problem is conversational or involves code generation. And no framework at all when your latency budget is under a few hundred milliseconds a hand-built state machine beats an abstraction layer there. The comparison table above breaks down where each one hurts.

You find out before your customers do. Every agent ships with an eval suite that scores accuracy on your real cases at each release, a dashboard for latency and token cost per task, human approval gates on high-stakes actions, and rollback to the last known-good version. Confidence thresholds route the uncertain case to a person instead of guessing.

Yes. Your VPC, your region, or fully air-gapped on-premise with open-weight models. No client data is used to train anything, and every tool call runs through a scoped service account with least-privilege access and a full audit log.

You do 100%, from day one, written into the contract. That covers the agent code, the prompt and graph definitions, the eval suite and the infrastructure as code. There is no licence to buy back and nothing locked to our accounts.

Yes. We connect agents through the Model Context Protocol, REST APIs, SQL databases and native connectors — Salesforce, HubSpot, SAP, Jira, Zendesk, Slack, Notion, Stripe, GitHub. If it has an API or an SDK, we can wire it in. No rip-and-replace.

The same engineers keep running it. Every engagement includes thirty days of hypercare, then an ongoing plan: model upgrades, prompt and cost tuning, eval maintenance as your data shifts. This is the part most agencies do not sell and most agents need model behaviour changes underneath you, and an unmaintained agent degrades quietly.