What does an AI agent development company actually build?

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.

How much does an AI agent cost to build?

Single-purpose agents typically run $8,000 to $25,000 for the build. Multi-agent systems with deep CRM and data-platform integrations run $25,000 to $60,000. Ongoing operations (model upgrades, eval refresh, drift monitoring, on-call engineer) cost 10 to 20% of build per month under our Managed Agents tier.

How long does it take an AI agent development company to ship to production?

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.

Which AI agent framework should we pick: LangChain, CrewAI, AutoGen, or OpenClaw?

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.

How do you prevent AI agents from hallucinating or going off-task in production?

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.

Can AI agents integrate with our existing CRM, ERP, or data warehouse?

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.

How do you measure AI agent performance in production?

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.

Will AI agents replace our team?

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.

Do you offer ongoing maintenance for AI agents once they ship?

Yes. Our Managed Agents tier ($3,500/mo) 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.

Can we start with one agent and expand later?

Yes. Most engagements start with one Single Agent ($8,000) to prove value, then expand into a Multi-Agent System ($25,000+) 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.

What happens to the code and IP if we stop working with Cubitrek?

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.

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.

Book a strategy callSee our work

4-8 wk

build to production

60%

cost reduction on support tickets

3×

pipeline velocity per SDR

100%

of agents ship with evals

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, delivered by senior operators.

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.

agent 01

LangChain / LangGraph

Our default for complex, stateful agents with branching workflows and many tools.

triggerGraph-based flow control and checkpointing required.

Agents that recover from failure and resume from the last good state.

agent 02

CrewAI

Multi-agent teams with role-based specialization (researcher, writer, reviewer, closer).

triggerWorkflow naturally decomposes into specialised roles.

Higher-quality outputs with visible reasoning per role.

agent 03

AutoGen

Microsoft's multi-agent framework for code-writing and problem-solving agents.

triggerDev tooling and technical research agents.

Agents that iterate, test, and correct their own output.

agent 04

OpenClaw

Open-source agent runtime with a fast-growing skill ecosystem. Default for browser-heavy and file-system work.

triggerAgents need to operate real applications end-to-end.

Agents that ship in days instead of weeks, operating on your actual files and apps.

agent 05

MCP (Model Context Protocol)

Anthropic's standard for letting agents discover and call external tools. The connective tissue of the agent economy.

triggerAgents need to call third-party services or expose their own skills.

Versioned, auth-gated MCP endpoints with auto-generated tool schemas.

agent 06

Custom / bespoke

Hand-rolled agent loops when none of the above fit the requirements.

triggerLatency-critical, cost-critical, or compliance-critical workloads where framework overhead is unacceptable.

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

A four-stage cadence that compounds every sprint.

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.

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.

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.

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 from recent programs.

Specific numbers from specific engagements. We can walk through unabridged case studies on the strategy call.

60%

tier-1 ticket resolution

DTC e-commerce client receiving 2,400 tickets/week across email + chat + Shopify Inbox. Multi-agent support system (triage agent, KB-lookup agent, escalation agent) shipped in 6 weeks. By week 12 the system was resolving 60% of tier-1 autonomously. Average response time dropped from 4.2 hours to 38 seconds.

3×

qualified meetings per SDR

B2B SaaS client running a 6-person SDR team. We shipped a sales-agent system (prospector, enricher, qualifier, scheduler) over 8 weeks. Per-SDR booked-meetings rate tripled. The team kept all 6 SDRs and moved them from cold outbound to mid-funnel conversion, where their close rate jumped from 18% to 31%.

-50%

research cycle time

Mid-market PE firm running due-diligence research. We shipped an overnight research-agent crew (sector researcher, financial researcher, news researcher, report writer) integrated with their internal knowledge base. Cycle time on each new target dropped from 12 days to 6 days, and report quality on the LP scorecard rose 23%.

Who we serve

Categories we know well.

Not a list of logos, a list of categories where we already speak the language and know the funnel.

SaaSE-commerceReal estateFintechHealthcareLegalProfessional servicesMarketplaces

Pricing

Transparent tiers. No hidden setup fees.

Month-to-month. Cancel anytime. All tiers include a dedicated delivery lead.

Single Agent

$8,000+

One workflow, scoped and shipped.

Single-purpose agent, production-ready
Framework selection + architecture
Evaluation harness with labeled data
Observability and tracing
Runbook + 30-day handover

Start with Single Agent

Real quotes from real engagements.

“Their agents do real work in our support queue every day. No demo theater, no babysitting.”

Head of Support

Series B fintech

Frequently asked questions

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.

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Ready when you are

Ready to start ai agents?

A 30-minute call. We map your goal, audit what exists, and come back with a scoped plan, usually within 72 hours.

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