AI AGENTS

Autonomous agents that
actually work in production.

We build AI agents that execute complex, multi-step workflows reliably — with guardrails, observability, and the engineering rigor that production demands.

WHAT WE BUILD

From single agents to full orchestration.

Task-Specific Agents

Single-purpose agents with deep tool integration for one critical workflow. Highly reliable, fast to deploy, easy to monitor.

Multi-Agent Pipelines

Specialized agents coordinating on complex tasks — researcher, analyst, writer, reviewer working in sequence or parallel.

Orchestration Systems

Central orchestrator routing tasks to the right agents with state management, error recovery, and human escalation.

Human-in-the-Loop

Agents that know when to ask for help. Confidence thresholds, approval gates, and escalation paths baked into the architecture.

Tool-Use Agents

Agents with real-world capabilities: web search, code execution, database queries, API calls, file manipulation, email, calendar.

Agent Monitoring

Full observability into every agent run — tool calls, reasoning traces, token usage, success rates, and failure analysis.

AGENT STACK

The tools behind production agents.

Orchestration Frameworks

LangGraphCrewAIAutoGenLangChain

Foundation Models

GPT-4oClaude 3.5 SonnetGemini 1.5 ProLLaMA 3

Tool Integrations

BrowsingCode ExecAPIsDatabasesEmailCalendar

Observability

LangSmithHeliconeArize PhoenixW&B Traces

Memory & State

RedisPostgreSQLPineconeQdrant

Infrastructure

FastAPICeleryDockerKubernetesAWSGCP

CASE STUDY

70% of manual work eliminated.

Multi-agent · Operations Automation

AI Agent Operations Suite

CLIENT: Quanta AI

70%

Manual work removed

Specialized agents

6 wks

To full deployment

99.2%

Task completion rate

Quanta's operations team was spending 60% of their time on repetitive, multi-system tasks: pulling data from multiple sources, reformatting, routing to the right teams, and following up. We built a 12-agent orchestration system that handles the full workflow with human escalation for edge cases.

Stack

CrewAILangChainGPT-4oPythonFastAPIPostgreSQLRedis

FAQ

Common agent questions.

Traditional automation follows fixed rules for known inputs. AI agents reason about tasks, choose tools dynamically, handle unexpected situations, and can complete multi-step workflows that require judgment — not just if/then logic.

Guardrails at every tool call, structured output validation, retry logic with exponential backoff, human-in-the-loop fallbacks for low-confidence decisions, and full audit trails. Reliability is an engineering problem, not a prompt engineering problem.

APIs, databases, file systems, web browsers, CRM systems, email, Slack, Notion, custom business tools — any system with a programmatic interface. We build custom tool wrappers where needed.

A single-purpose agent with 3–5 tools typically takes 4–6 weeks from discovery to production. Multi-agent orchestration systems with human-in-the-loop controls typically run 8–12 weeks.

BUILD YOUR AGENT

Automate your most complex workflows.

Tell us what you want to automate. We'll design the agent architecture and give you a fixed quote.