AI agent development company for production workloads
Agents that close, not agents that demo.
AI agent development company building custom autonomous agents for sales, support, ops, and research. LangChain, CrewAI, AutoGen, OpenClaw, MCP. Senior engineers shipping to production with evals, guardrails, tracing, and a runbook from day one.
- 4-8 wkbuild to production
- 100%of agents ship with an evaluation set
- Typedtools with validation in code, not in a prompt
- Definedhuman handover, agreed before launch
AI agent development is the practice of building software that perceives inputs, reasons about a goal, calls tools, and takes action on its own instead of waiting for a person to click through each step. Cubitrek builds sales, support, research, and ops agents on LangChain, CrewAI, AutoGen, OpenClaw, and MCP, and every one ships with an evaluation harness, guardrails, and tracing. Most AI agent development companies ship demos that collapse under real inputs. Ours stay up on day 90. We build every agent like production software: real-data evaluations, hard guardrails, full tracing, no demo theatre. Senior engineers who have shipped LangChain, CrewAI, AutoGen, and OpenClaw into production for revenue teams.
What we ship
Everything under one roof.
Sales agents
Qualify leads, enrich profiles, schedule meetings, keep pipeline clean. Agents that earn their keep by booking real meetings. CRM integrations (HubSpot, Salesforce, Pipedrive, Attio) ship as standard.
Support agents
Resolve 40 to 70% of tier-1 tickets across email, chat, Slack, Discord, and Zendesk. Escalate the rest with full context. Write their own playbooks from resolution transcripts.
Research agents
Competitive intel, market research, due diligence, literature reviews. Run overnight against fresh sources. Deliver structured briefs with citations, not data dumps.
Ops agents
Internal workflows across Slack, Notion, Jira, Linear, GitHub, and your CRM. Status updates, follow-ups, onboarding, compliance checks on autopilot. 24/7.
Multi-agent orchestration
Teams of specialist agents under a supervisor. Researcher plus writer plus reviewer. Or prospector plus qualifier plus closer. Cross-agent memory, parallel execution, recovery from failure.
Evals and guardrails
Every agent ships with an evaluation suite against labeled real-world data. Plus prompt-injection defense, PII handling, rate limits, and an anomaly circuit breaker that halts on out-of-distribution inputs.
MCP-native agents
Model Context Protocol support out of the box. Your agents expose their skills as MCP endpoints other agents can call, and consume MCP endpoints from third-party services. Reusable across the agent economy.
Observability and tracing
Full tracing via LangSmith, Langfuse, or Phoenix. Per-action latency, cost, success rate, and reasoning trace. Anomaly detection paged to on-call. Debugging an agent in production looks like debugging any other distributed system.
Shadow-mode rollouts
Every agent ships through three phases: shadow (agent runs, human takes action), human-in-the-loop (agent takes action, human reviews), autonomous (agent owns the loop). Production confidence is earned, not assumed.
How we build
The frameworks we pick, and why.
Framework selection is an engineering decision, not a fashion one. We match the tool to the workload. We run all five in production and know exactly where each one breaks.
LangChain / LangGraph
Our default for complex, stateful agents with branching workflows and many tools.
- Trigger
- Graph-based flow control and checkpointing required.
- Output
- Agents that recover from failure and resume from the last good state.
CrewAI
Multi-agent teams with role-based specialization (researcher, writer, reviewer, closer).
- Trigger
- Workflow naturally decomposes into specialised roles.
- Output
- Higher-quality outputs with visible reasoning per role.
AutoGen
Microsoft's multi-agent framework for code-writing and problem-solving agents.
- Trigger
- Dev tooling and technical research agents.
- Output
- Agents that iterate, test, and correct their own output.
OpenClaw
Open-source agent runtime with a fast-growing skill ecosystem. Default for browser-heavy and file-system work.
- Trigger
- Agents need to operate real applications end-to-end.
- Output
- Agents that ship in days instead of weeks, operating on your actual files and apps.
MCP (Model Context Protocol)
Anthropic's standard for letting agents discover and call external tools. The connective tissue of the agent economy.
- Trigger
- Agents need to call third-party services or expose their own skills.
- Output
- Versioned, auth-gated MCP endpoints with auto-generated tool schemas.
Custom / bespoke
Hand-rolled agent loops when none of the above fit the requirements.
- Trigger
- Latency-critical, cost-critical, or compliance-critical workloads where framework overhead is unacceptable.
- Output
- Lean Python or TypeScript agents tuned for the specific workload.
We run all five in production. We know where each one breaks.
How we work
How the work actually runs.
- 01
Scope one agent
Pick one workflow with measurable value (revenue lift, cost cut, or cycle-time reduction). We write the eval spec before we write code. No agent ships without a labeled-data test set.
- 02
Build and evaluate
Four to 8 weeks of engineering. Weekly eval runs against labeled real-world data. You see the accuracy graph before we ship. Framework choice locked in week 1 based on workload.
- 03
Ship and observe
Shadow mode first, then human-in-the-loop, then autonomous. Full tracing with LangSmith, Langfuse, or Phoenix. On-call engineer included for the first 30 days of production.
- 04
Expand
Additional agents plug into the same eval and observability stack. Cross-agent memory via shared state or MCP. The first agent is the platform; everything after compounds.
What good looks like
Representative outcomes.
Directional numbers from real programs. On the call we walk through the case studies behind them, method included.
- tool contracts with validation in code, not instructions in a prompt
- Typedtool contracts with validation in code, not instructions in a prompt
- of real questions, rerun after every prompt or model change
- Eval setof real questions, rerun after every prompt or model change
- handover defined before launch, not bolted on after
- Humanhandover defined before launch, not bolted on after
Who we serve
Categories we already know.
- SaaS
- E-commerce
- Real estate
- Fintech
- Healthcare
- Legal
- Professional services
- Marketplaces
What clients say
I was particularly impressed by their creative approach and attention to detail.
Videography & photography company · website, SEO + design
They delivered the project on time.
Watch retailer · Shopify store build
Cubitrek always had a positive mindset and was kind.
Personal training company · video + social media
Questions buyers ask us.
- An AI agent is software that perceives inputs, reasons about goals, uses tools, and takes actions autonomously to complete a multi-step task. A chatbot only responds; RPA only follows scripts; an agent makes decisions based on context. Our agent development services cover sales agents, support agents, research agents, ops agents, and multi-agent systems for everything in between. Built on LangChain, CrewAI, AutoGen, OpenClaw, or hand-rolled depending on the workload.
- It depends on scope. A single-purpose agent costs less than a multi-agent system with deep CRM and data-platform integrations, and ongoing operations (model upgrades, eval refresh, drift monitoring, an on-call engineer) run under our Managed Agents tier. We quote after a short scoping call.
- Simple agents with a clear workflow and clear eval ship in 4 to 6 weeks. Complex multi-agent systems with novel integrations ship in 8 to 12 weeks. We always run shadow mode first, then human-in-the-loop, then autonomous, so production confidence is earned in stages rather than gambled on a launch.
- It depends on the workload. LangGraph fits complex stateful workflows with branching and recovery. CrewAI fits role-based agent teams (researcher, writer, reviewer). AutoGen fits code-writing and technical research agents that need to iterate on their own output. OpenClaw fits browser-heavy and file-system work where agents drive real applications. We pick per project based on what survives production, not based on framework hype.
- Four layers of defence. Structured outputs with JSON schema validation. Tool-use guardrails that prevent agents from calling tools they should not. Prompt-injection defense at the input layer. An anomaly circuit breaker that halts execution on out-of-distribution inputs. We pair this with eval-driven development: every agent ships with a labeled test set, and the tests run against every model upgrade and prompt change.
- MCP is Anthropic's open standard for letting AI agents discover and call external tools. Released late 2024 and adopted across Claude, ChatGPT, OpenClaw, and most major agent frameworks by mid-2026. Practical impact: your agents expose their skills as MCP endpoints other agents can call (an external sales agent can call your lead-enrichment agent), and consume third-party MCP endpoints (your agents call Stripe, GitHub, Linear, your CRM via MCP instead of bespoke API integrations). Every agent we build ships MCP-ready.
- Yes. We have shipped agents with deep integrations to HubSpot, Salesforce, Pipedrive, Attio, Notion, Slack, Discord, GitHub, Linear, Jira, Snowflake, BigQuery, Postgres, and a long tail of vertical SaaS. Most integrations are MCP-first in 2026; legacy systems still need REST or webhook adapters which we build as part of the engagement.
- Three layers of metrics. Per-action: latency, cost per call, success rate, tool-call accuracy. Per-task: task completion rate, escalation rate, user-reported quality. Business outcome: the metric the business cares about (revenue per agent, ticket deflection rate, cycle-time reduction). All three roll into one observability dashboard via LangSmith, Langfuse, or Phoenix.
- They remove the repetitive 60 to 70% of tasks your team does today. Your team focuses on the 30% that needs judgment, relationships, and creativity. Our clients reinvest the savings into growth, not layoffs: the e-commerce client in the case study above kept all 12 support staff and moved them from tier-1 ticket grind to customer-success outreach, where they drove a measurable retention lift.
- Yes. Our Managed Agents tier includes 24/7 uptime monitoring, monthly eval refresh against new labeled data, model upgrades when better models ship, prompt and guardrail tuning, and an on-call engineer for incidents. Agents drift; new edge cases appear; underlying models change. Maintenance is not optional for any agent in production.
- Yes. Most engagements start with one Single Agent to prove value, then expand into a Multi-Agent System once the first agent is in production and the business has confidence. The first agent is the platform; everything after compounds because the eval harness, observability, integrations, and guardrails are reusable across agents.
- You own the code, the prompts, the eval data, and the runbooks. We hand over a self-contained repository with deployment scripts, documentation, and 30 days of handover support. Engagement is month-to-month with 30-day notice. The whole point of building your own agents (instead of paying a per-seat agent platform) is that you control the asset.
Ready when you are
Ready to start AI agents?
A 15-minute call. We map the goal, look at what exists, and come back with a scoped plan.