Agentic AI Development Services

Agentic AI development builds software that pursues a goal rather than following a fixed script: it plans its own steps, chooses which tool to call, checks whether the result was successful, and replans when it wasn't.

The engineering work is less about the model and more about everything around it, namely the tools it can access, the memory it retains, the permissions it holds, and the points at which a human signs off.

Our agentic AI development services cover that full stack, from the first architecture decision through evaluation, deployment, and the monitoring that keeps an agent trustworthy after launch.

A single agent holds one goal, one toolset, and one thread of context, which suits work a capable person could do end to end with the right access. A multi-agent system splits the work across specialists with a supervisor coordinating them, which suits work too broad for one context window or one permission set.

Most engagements start with a single agent, because a multi-agent system built before the single-agent case is proven usually adds coordination failure on top of an unsolved problem.

They start from opposite ends. This page starts from the agent: its architecture, tools, memory, permissions, and oversight. AI Automation starts from the business process: which workflow is expensive, what its exceptions are, and what changes when it stops being handled by hand.

If you can name the process that's hurting, start there. If you know you need agent architecture designed and built, start here. Plenty of engagements need both, and we'll tell you which you actually need during scoping.

By deciding in advance what it may do. Each agent gets an explicit tool list, scoped credentials per integration rather than a shared service account, and hard limits on the actions available to it. High-consequence steps pause at an approval queue with the full reasoning trail attached, so a person makes the call.

Beyond that, output validators check results against schemas and business rules before anything commits, every action lands in an immutable log, and a kill switch stops the agent in one click.

Per industry averages across 2026 pricing guides, a proof of concept on a single agent runs roughly $15,000 to $35,000 over four to six weeks. A production agent with real tool access, evaluation, and oversight lands around $60,000 to $150,000, which is where most mid-market builds sit. A multi-agent system or enterprise agent platform starts near $150,000 and runs past $500,000, depending on how many systems it touches, the compliance scope, and how much autonomy it is trusted with.

Budget for running it, not only building it. Industry figures put ongoing inference, monitoring, and maintenance at 15 to 30% of the build cost per year, which is the line most quotes leave out. Custom work doesn't fit a fixed price list, so rather than quote a misleading number, we scope your use case and give a tailored estimate.

Our scope generator will give you an approximate figure and a proposed architecture in minutes, without a call.

A working proof of concept lands in about a week, and a functional agent in production in roughly a month for a single agent with two or three integrations. The early days go on scope and a thin end-to-end path; the rest goes on the parts that make it trustworthy, namely error recovery, validators, evaluation, and observability.

We built our own production agent, with 16 live tools on the Model Context Protocol, in four weeks with one developer, so this is a schedule we hold ourselves to rather than an estimate.

Yes, completely. You own the source code, the prompts, the tool definitions, the evaluation suites, and any fine-tuned weights, with full rights transferred to you. There's no vendor lock-in: you can host, extend, or move the work to another team whenever you want.

Through documented, authenticated integrations rather than screen scraping. We connect agents to Salesforce, NetSuite, Jira, internal APIs, and databases using the Model Context Protocol where it fits, with scoped credentials per integration so an agent reaches only what its job requires, and full traceability on every call it makes.

Yes. For regulated or sensitive data we deploy the whole stack in your cloud, on-premise, or air-gapped, using open-weight models where a third-party API would be inappropriate. We built exactly this for Sylo: an agent platform packaged as a single binary that runs on the user's own hardware, where conversations, files, memory, and credentials never leave the machine.

With fixed evaluation suites rather than impressions. We agree the tasks an agent must complete, score runs against them for correctness, tool selection, and recovery, and re-run the suite whenever a prompt, model, or provider changes. Every tool call is traced in Langfuse, so a regression shows up as a number rather than a complaint.

We also track how often humans override the agent, because a rising override rate is usually the earliest signal that something upstream has drifted.

No. We build on LangGraph, the Vercel AI SDK, and the Model Context Protocol because they are production-tested, but the model provider sits behind an abstraction, so moving between OpenAI, Anthropic, Google, Llama, or Mistral is a configuration change rather than a rebuild. We run our own products this way, routing across multiple providers in production.

We serve a variety of industries, including:

• Healthcare operations (claims triage, patient intake, clinical documentation)

• Finance (reconciliation, invoice handling, research assistants)

• Legaltech (contract review, multi-jurisdiction research)

• PropTech (portfolio analysis, tenant support)

• Recruitment (candidate screening and enrichment)

• Internal engineering and support copilots

The pattern that suits an agent: multi-step work, a clear definition of done, tools it can call to get there, and a cost per case worth measuring.