We build AI agents for production — with the memory, tool access, integrations, and guardrails to handle real workflows. Not chatbots. Not prototypes. Agents that finish the job.
Trusted by teams shipping production software
We build the production version. Custom agents wired into your real systems — CRM, helpdesk, database, payments — with memory across sessions, graceful human escalation, and full observability so you can see exactly what the agent did and why.
Everyone uses ChatGPT solo.
Your whole team is on chat independently — no shared context, no central brain, the same prompts reinvented a dozen times over.
One person is the answer desk.
The same standard questions land on the same person all day. The knowledge lives in their head, not a system anyone can actually query.
Chat doesn't know your business.
ChatGPT can't see your CRM, your docs, or your customer data — so it can't answer anything specific to how you actually run.
Every agent we ship has these moving parts. We don't ship raw LLM wrappers and call them agents — production agents need all of this.
- 01
Tool access via MCP
We wire the agent into your real systems via MCP (Model Context Protocol) so it can read and write CRM records, query databases, send emails, post to Slack — whatever the workflow needs.
- 02
Persistent memory
Conversation memory across sessions, user-specific context, and long-term memory of facts and preferences. The agent remembers who it talked to and what was said.
- 03
Structured outputs
Where the agent's output drives downstream code, we use JSON schemas and Zod validation so the result is predictable, not creative.
- 04
Human-in-the-loop
Confidence-based escalation: when the agent isn't sure, it hands off to a human with full context. No black-box hallucinations into your customer's inbox.
- 05
RAG for your data
Retrieval-augmented generation against your docs, knowledge base, or product data — so the agent answers from your information, not from training data.
- 06
Observability
Full traces of every agent run: what was asked, what tools were called, what the model thought. Built on LangSmith, Helicone, or custom logging.
- 07
Evals + testing
We build evaluation suites that test the agent against real-world cases before deployment. Quality is measured, not assumed.
- 08
Cost & latency control
We pick the right model for each step (Haiku for routing, Sonnet for reasoning, Opus for hard cases). Caching, batching, and budget alerts so model costs don't run wild.
- 09
Deployment + monitoring
We ship the agent to Vercel, AWS, or your infra. Monitoring, alerting, model failover, and a kill switch when needed.
Most agent builds fail because they skip discovery and go straight to prompting. We don't.
Discover the workflow
1 week. We map the actual task end-to-end with your team. Where humans currently do it, where it breaks, what 'good' looks like. Most agents fail because this step gets skipped.
Architect the agent
1 week. Tool inventory, model choice per step, memory strategy, escalation rules, eval criteria. Documented before any code is written.
Build the MVP
2–3 weeks. First version handles the happy path against real data, with observability and a basic UI. Your team starts testing it immediately.
Harden + ship
2–4 weeks. We fix the edge cases the MVP exposed, build the eval suite, deploy to production with monitoring, and train your team.
Run
Ongoing. Model upgrades, prompt tuning, new tools, scope expansions. Most agent budgets shift from build to run after month 2.
We're model- and framework-agnostic but we have strong defaults. We pick based on the job, not the trend.
- MCPMCPTool protocol
- LangChain / LangGraphAgent framework
- Vercel AI SDKAgent framework
- Anthropic SDKSDK
- OpenAI SDKSDK
- Claude (Opus/Sonnet/Haiku)Reasoning model
- GPT-4o / o1Reasoning model
- LlamaOpen-source LLM
- MistralOpen-source LLM
- REST / GraphQLAPI layer
- WHWebhooksEvent triggers
- BABrowser automationHeadless tooling
- pgvectorVector store
- PCPineconeVector store
- WVWeaviateVector store
- PostgresDatabase
- LangSmithTracing
- HeHeliconeLLM analytics
- LangfuseTracing
- {}Custom tracesLogging
- VercelHosting
- AWSAWS LambdaServerless
- Cloudflare WorkersEdge runtime
- ModalGPU compute
“Futur Labs shipped in six weeks what our internal team couldn't in eighteen months.”
Focused Agent
One agent, one workflow. Customer-support triage, sales outreach, internal-ops automation, data analysis — whatever the highest-value use case is.
- 1 agent + integrations
- Memory + RAG if needed
- Eval suite + observability
- 4–6 weeks to ship
- 30-day post-launch support
Arlo
Our own product. An MCP connector that lets Claude query 100+ analytics platforms in natural language. Built for agencies managing dozens of clients. Pass-through architecture — never stores client data.
Agency ERP
Agents embedded in our own ERP for client triage, scope review, and project status reporting. Same architecture we ship for clients.
Nico
Placeholder card for testing the 3-up layout. Swap in a real product when ready.
An AI agent is a system that uses an LLM to reason about a task, call tools (APIs, databases, browsers), maintain memory across interactions, and act autonomously toward a goal. Not just a chatbot — an agent can actually do things: send emails, update records, run reports, escalate edge cases. We build the production-grade version of that.
We're model-agnostic. We default to Anthropic Claude for reasoning and tool use (we've shipped a lot on it), OpenAI when the task demands it, and self-hosted open models when cost or compliance requires. On the framework side: LangChain, LangGraph, the Vercel AI SDK, the Anthropic SDK directly, MCP for connecting tools. We pick based on the job, not loyalty.
Custom AI agents have access to your data, your tools, and your business logic. They remember context across sessions, hand off to humans on edge cases, and integrate with the systems your team already uses. A GPT or a generic chatbot can't touch your CRM, can't book meetings on your calendar, and can't actually finish multi-step work.
Focused agents start around $15k for a single workflow (e.g., customer-support triage, sales outreach, internal-ops automation). Multi-agent systems with custom integrations and memory run $30–80k depending on scope. Ongoing run cost is usually $200–$2k/month in model fees — we'll model it for you before you commit.
Yes. We built Arlo — an MCP connector that lets Claude query 100+ analytics platforms in natural language. It's running for agencies managing dozens of clients. The architecture pattern (MCP tools + Claude + pass-through data access) is the same one we use for client agent builds.
This is most of the actual work. We build agents with structured outputs, validation, human-in-the-loop escalation, and observability so you can see what the agent is doing and why. We test against real data, not just happy paths. The honest answer: agents are best for tasks where 'mostly right' is fine and humans review the edge cases.
A few questions about the project so we come prepared — then we'll set up a short call to dig in.