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— AI AGENTS DEVELOPMENT

Agents that actually get work done.

We build autonomous AI agents that plan, reason, call tools, and complete multi-step tasks — with real guardrails, observability, human handoff, and cost control engineered in from day one.

25+

Agent systems shipped

92%

Avg. task success rate

14K+

Tasks/day processed

0

Runaway incidents

01 / CAPABILITIES

Agents we build.

Beyond toy demos — production agent systems doing real work with real consequences.

Support Agents

Multi-turn customer support with access to knowledge bases, ticketing systems, and human handoff.

Coding Agents

Agents that read codebases, write code, run tests, debug failures, and open PRs autonomously.

Sales Agents

Research prospects, draft personalized outreach, schedule meetings, update CRM, track engagement.

Research Agents

Multi-source research with web search, document analysis, citation, and structured report generation.

Ops Agents

Monitor systems, diagnose issues, execute runbooks, and escalate to humans with full context.

Multi-Agent Systems

Orchestrated agent teams with specialized roles, shared memory, and coordinator agents.

02 / PROCESS

Building reliable agents.

Four stages that separate demo agents from production agents.

STEP 01

Define the task

Exactly what does the agent need to do? What tools? What success criteria? What failure modes? We scope precisely.

STEP 02

Build + test

Tool definitions, prompt engineering, chain-of-thought design. Test on hundreds of simulated scenarios before production.

STEP 03

Guardrails + monitoring

Rate limits, cost caps, dangerous-action confirmations, human review queues, full execution traces.

STEP 04

Ship + evaluate

Deploy with observability. Track success rate, cost, latency, edge cases. Continuous improvement based on real usage.

03 / STACK

The agent stack.

Our toolkit for building reliable production agent systems.

LangChain

Agent orchestration

LangGraph

Stateful workflows

OpenAI Assistants

Function-calling

Claude

Long-context reasoning

Temporal

Durable execution

Browserbase

Headless browsing

LangSmith

Agent tracing

Helicone

Cost + performance

04 / USE CASES

AI agents for real business workflows.

We build autonomous AI agents that automate repetitive tasks, improve productivity, and support business operations across departments.

Customer Support Agents

Handle customer inquiries, retrieve knowledge, escalate issues, and provide 24/7 assistance.

Sales & Lead Qualification

Identify opportunities, qualify leads, schedule meetings, and assist sales teams.

Research Assistants

Analyze information, summarize findings, and accelerate business research workflows.

Operations Automation

Execute repetitive operational tasks across internal systems and business processes.

Developer Agents

Assist with code generation, documentation, testing, and engineering workflows.

Multi-Agent Systems

Coordinate multiple specialized agents that collaborate on complex business objectives.

05 / SECURITY

Security built for autonomous execution.

AI agents often interact with business systems, APIs, and sensitive workflows. Security and control mechanisms are built into every deployment.

Role-Based Permissions

Control agent access to tools, systems, and sensitive operations.

Human Approval Workflows

Require human validation before critical actions are executed.

Tool Access Controls

Restrict agent permissions and API interactions based on business rules.

Audit Logging

Track decisions, actions, tool usage, and workflow execution history.

Guardrails & Validation

Input filtering, output validation, and operational safeguards.

Monitoring & Observability

Visibility into agent behavior, performance, costs, and operational health.

06 / WHY CHOOSE US

Built for production agents.

Building AI agents requires more than prompt engineering. We combine reasoning systems, tooling, guardrails, and operational controls.

Agent Engineering Expertise

Deep experience building autonomous agents with reasoning, planning, memory, and tool execution capabilities.

Production-Ready Architecture

Designed for reliability, scalability, observability, and long-term operation in real business environments.

Human-in-the-Loop Controls

Approval workflows and oversight mechanisms that keep critical decisions under human supervision.

Multi-Agent Orchestration

Coordinate specialized agents that collaborate to solve complex tasks and business processes.

Scalable Infrastructure

Cloud-native architecture built to support growing workloads, users, and operational demands.

Long-Term Technical Partnership

Ongoing optimization, monitoring, maintenance, and expansion as your AI capabilities evolve.

07 / RESOURCES

Explore related AI services.

Discover technologies that help organizations build intelligent, scalable AI systems.

AI Agents Development

Build autonomous AI agents capable of planning, reasoning, tool execution, and multi-step workflows.

  • Multi-step reasoning
  • Tool execution
  • Human-in-the-loop
  • Agent orchestration

RAG Systems Development

Connect AI models to business knowledge through retrieval-augmented generation systems.

  • Vector search
  • Knowledge retrieval
  • Source grounding
  • Enterprise search

AI SaaS Development

Build AI-powered software platforms with multi-tenant architecture and cloud infrastructure.

  • SaaS architecture
  • Billing systems
  • Admin dashboards
  • Cloud deployment
08 / FAQ

Common questions.

What makes a 'production' agent vs. a demo?+
Demos work on happy-path examples. Production agents handle edge cases, errors, timeouts, adversarial inputs, cost spikes, and infinite loops gracefully. They have observability, guardrails, and human escalation paths. The engineering to make them reliable is 10x the work of the initial prototype.
How do you prevent runaway costs?+
Per-agent budget caps, per-task budget caps, tool-use rate limits, model routing (use cheaper models for simpler steps), and automatic shutdown on budget exhaustion. We also cache LLM calls aggressively.
Can the agent take dangerous actions autonomously?+
Dangerous actions require human confirmation by default — send email, transfer money, delete data, call external APIs with side effects. We define the dangerous actions in scope and enforce confirmation via tool design.
How do you measure agent quality?+
Success rate on golden test sets, cost per task, latency percentiles, human-review pass rate, user feedback signals. We build evaluation dashboards tailored to each agent's use case.
Can you build multi-agent systems?+
Yes — specialist agents with coordinator agents, shared memory, task decomposition, and inter-agent communication protocols. Increasingly useful for complex workflows. We've built coding teams, research teams, and ops teams as multi-agent systems.
— READY TO START?

Build agents that do real work.

Tell us what task you’d automate. We’ll tell you honestly if an agent is the right tool, and scope the build.

Multi-Step Reasoning
Human-in-the-Loop Controls
Production Agent Infrastructure