AI Agent Development Services for Enterprise Teams

Section 1

What an AI agent is, and when you need one

An AI agent is software that can take a goal, decide on a sequence of actions, use tools and data on its own, and produce a result without a human stepping through every move.

That sounds simple. In practice, getting an agent to behave reliably inside a real business — connected to real systems, with real data, accountable for real outcomes — is where most projects stall.

You probably need an AI agent (and not just a chatbot or an automation script) when:

A workflow has branching decisions that change based on context, not fixed rules.
The work requires reading, reasoning, and writing across multiple systems (CRM, ticketing, ERP, email, documents).
A human currently does the work, and that human's time is the bottleneck.
The cost of a wrong answer is recoverable — i.e., the agent operates with a human-in-the-loop or a defined rollback rather than fully unattended.

If your problem is “answer a question from a knowledge base,” you want a chatbot. If your problem is “triage 200 inbound requests, decide what each one needs, and act on most of them,” you want an agent.

Section 2

Brilworks's AI agent development services

We deliver custom AI agents end-to-end. Four engagement modes, the same outcome: an agent running in production with measurable impact.

Custom AI agent design

We start with the workflow, not the model. We map the work being done today, identify the decisions that drive it, and design the smallest agent that can take ownership of the right slice. You get a written agent specification — goal, tools, data sources, guardrails, evaluation criteria — before a line of code is written.

Agent build

We build the agent against the specification — model selection, tool integration, prompt and policy engineering, observability, and evaluation harnesses. Every agent we ship has an evaluation suite committed to source control on day one, so you can verify behavior on every change.

Integration and deployment

Agents only matter when they're plugged into the systems where work actually happens. We integrate with your CRM, ticketing, data warehouse, internal APIs, and identity stack. Deployment is your choice — your cloud, our cloud, hybrid. We ship to production, not to a sandbox.

Ongoing improvement

An agent's first version is rarely its best. We instrument every agent for evaluation, error rates, escalation rates, and unit cost. We then iterate: tighter prompts, better tools, narrower scope, broader scope — whatever the data says.

Section 3

Where custom AI agents earn their keep

Four use case categories where we’ve shipped, with the kind of result we measure for. (Specific client names withheld under NDA — case studies available on request, signed under MNDA.)

Customer operations

Triage and resolution agents that read inbound tickets, classify intent, pull relevant context from CRM and product systems, and either resolve directly or hand off to the right human with a draft response prepared. Result we target: 30–60% reduction in time-to-first-response, measurable deflection on tier-1 tickets.

Sales and revenue

Research and outreach agents that build account briefs, summarize signal across data providers, and prepare the first-pass outreach draft. Result we target: SDR research time cut by 50%+, with the human still owning the send.

Internal operations

Back-office agents for procurement intake, contract triage, vendor onboarding, expense flagging — the long tail of internal workflows that nobody owns and everybody hates. Result we target: cycle-time reduction on workflows that previously required cross-team chasing.

Engineering and product

Coding and code-review agents that work alongside dev teams. We've helped existing clients adopt Claude Code the right way, with anonymized client data showing coding-speed lifts of roughly 50% across two engagements (full case study forthcoming). See our dedicated Agentic AI Software Development service for the dev-tooling vertical.

Section 4

Tech stack we work with

We are model-, framework-, and cloud-agnostic. We pick the stack that fits the job, not the other way around.

Foundation modelsAnthropic Claude (Opus, Sonnet, Haiku), OpenAI (GPT-4 family), AWS Bedrock, Azure OpenAI, open-weights (Llama, Mistral) when latency, cost, or data-residency requires it.
Agent frameworks and runtimesClaude Agent SDK, OpenAI Agents SDK, LangGraph, custom orchestration when frameworks get in the way. Internally we also operate Hermes and OpenClaw (our own agent runtimes) — useful when a client needs deeper control than off-the-shelf frameworks give.
Tool layerMCP (Model Context Protocol) for tool integration, custom tool servers, retrieval pipelines (vector DB of your choice — pgvector, Pinecone, Weaviate, OpenSearch).
Evaluation and observabilityLangfuse, Braintrust, Helicone, OpenTelemetry traces, plus our own evaluation harnesses for production behavior monitoring.
DeploymentAWS, GCP, Azure, on-prem when the data demands it. Containerized by default, Terraform-managed.

Section 5

Engagement model

Three engagement modes. Pick the one that matches how committed you already are.

Discovery sprint — 2 weeks

For teams who know they want an AI agent but aren't sure which workflow is the right first target. We map the candidate workflows, score them on impact and feasibility, and deliver a written agent specification for the top one. Outcome: a go/no-go decision and a build-ready spec.

Build engagement — 6–12 weeks

Spec → working agent in production. Fixed scope, fixed timeline, weekly demos. Includes evaluation harness, observability, and a 30-day post-deploy stabilization window.

Embedded team — ongoing

For organizations with multiple agent workflows in flight. A senior agent engineer plus a delivery lead embed with your team, ship agents on a continuous cadence, and own the agent platform decisions. Monthly retainer, scoped quarterly.

All engagements include source code, evaluation suites, runbooks, and a knowledge transfer at handoff. No black boxes.

Section 6

Why Brilworks

We ship to production.
Every engagement ends with an agent running in your environment, with eval and monitoring, not with a deck.
We're stack-agnostic.
We don't have a model partner pushing us to oversell theirs. The model picks the project, not the other way around.
We use what we sell.
Brilworks runs internal agents (Hermes and OpenClaw) for our own marketing, ops, and engineering work. We're our first customer.
We measure.
Eval rates, escalation rates, cost-per-resolution. If we can't agree on a metric before kickoff, we won't take the engagement.

Section 7

Talk to us

If you have a workflow in mind — or if you suspect there’s one and want a second opinion — book a 30-minute scoping call. No deck, no sales engineer, just a conversation with the engineer who would lead your build.

Book a scoping call Or send a one-line brief →

Frequently Asked Questions

A typical first agent ships to production in 6–12 weeks from spec lock. Discovery sprints add 2 weeks at the front. Pace depends mostly on integration depth and access to the systems the agent needs to read and write.

Yes. Every engagement transfers full IP and source code at completion, including the agent specification, prompts, tool integrations, and evaluation harnesses. No vendor lock-in.

We are model-agnostic. We commonly ship on Claude (Anthropic), GPT-4 (OpenAI), or Bedrock. Open-weights models (Llama, Mistral) are a fit when latency, cost-per-call, or data residency makes them the right call. We make the recommendation based on your constraints, not on a partner program.

Every agent ships with an evaluation suite — a set of representative inputs with expected behavior — plus production observability. We agree on success metrics before kickoff (e.g., resolution rate, escalation rate, cost per resolution, time-to-first-response). You see those metrics on a dashboard from day one in production.

Yes. If you already have a model gateway, vector DB, agent framework, or observability stack, we plug in. If you don't, we recommend a minimal stack and you keep it.

A chatbot answers questions. An agent decides on a sequence of actions, uses tools and data on its own, and produces a result. If your problem is "answer a question," a chatbot is enough. If your problem is "do the work," you want an agent.

We deploy into your environment when compliance requires it (your VPC, on-prem, BYOC). We work with SOC 2, HIPAA, and GDPR-bound clients regularly. We sign MNDAs ahead of any reference call.

Discovery sprints are fixed-fee. Build engagements are fixed-fee scoped against the spec. Embedded retainers are monthly. We share rate cards on the scoping call so they're attached to a real scope, not a hypothetical.