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Aixo LabAixo Lab

AI Agent Development

We build enterprise AI agents — systems that plan, call tools, and take real action in your existing software — architected for reliability, oversight, and production use, not chat demos.

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  • Multi-Agent Systems
  • Tool Calling
  • Human-in-the-Loop
  • RAG
  • Enterprise Security
Overview

Our approach

We build enterprise AI agents — systems that plan, call tools, and take real action in your existing software — architected for reliability, oversight, and production use, not chat demos.

  • Beyond Chat: Agents That Take Action

    An agent calls real functions and changes real state in your systems, instead of describing what a human should do next.

  • Planning & Multi-Step Reasoning

    Tasks broken into steps and re-planned as conditions change, not a single prompt-response pair pretending to be autonomous.

  • Grounded in Your Own Systems

    Tool calling and retrieval connect an agent to your actual data and APIs, not a generic knowledge base.

  • Human Oversight Where It Matters

    Escalation and approval steps built in for decisions that carry real risk, not full autonomy by default.

  • Built for Multi-Agent Coordination

    Specialized agents with clear responsibilities, coordinated deliberately, when a single agent genuinely isn't the right shape.

  • Engineered for Production, Not Demos

    The same reliability, monitoring, and security standard we apply to every backend system we build.

What's Included

Everything under one roof

Everything included in this engagement, from architecture to long-term support — one team, one system.

Customer Support Agents

Agents grounded in your documentation and policies, with a clear handoff to a human when a case needs one.

Internal Knowledge Agents

Agents that answer questions and retrieve information from internal documentation, wikis, and systems.

Sales Assistants

Agents that qualify leads, draft outreach, and update CRM records as part of an existing sales workflow.

HR Assistants

Agents that handle policy questions, onboarding steps, and internal requests within defined boundaries.

Document Processing Agents

Agents that extract, classify, and route data from contracts, invoices, and forms through a review workflow.

Workflow Automation

Agents that execute multi-step operational processes, combining AI reasoning with deterministic logic.

Research Agents

Agents that gather and synthesize information from multiple sources into a structured, reviewable output.

Reporting Agents

Agents that assemble and summarize operational data into reports on a schedule or on request.

Operations Agents

Agents that monitor operational systems and take predefined actions when specific conditions are met.

Multi-Agent Systems

Coordinated systems of specialized agents for processes too complex or varied for a single agent to own.
Why Aixo Lab

Why companies choose Aixo Lab

  1. We Engineer Agent Architecture, Not Just Prompts

    Planning logic, tool boundaries, and state management are designed as software architecture, not tuned through prompt iteration alone.

  2. We Design Multi-Agent Systems With Clear Boundaries

    Each agent gets a defined responsibility and interface, because coordination overhead without clear boundaries creates more problems than it solves.

  3. We Build Human-in-the-Loop by Default

    Approval and escalation paths for consequential actions are part of the initial design, not a safety net added after something goes wrong.

  4. We Treat Observability and Evaluation as Core Infrastructure

    You can see what an agent decided and why, and we test it against real scenarios before it touches production data.

  5. We Hand Off Code Your Team Can Own

    Clear architecture and documentation mean your own engineers — or ours, later — can extend this system without archaeology.

Our Capabilities

Our AI Agent Capabilities

The specific technical capabilities behind every AI agent engagement — not a generic feature list, the actual engineering surface we work in daily.

AI Agents

Systems that plan, call tools, and take action toward a goal, built with defined scope rather than open-ended autonomy.

Autonomous Workflows

Multi-step processes an agent can execute end to end within clearly defined limits, with a human checkpoint where it matters.

Multi-Agent Systems

Specialized agents coordinated through defined interfaces, each with a scoped responsibility rather than one agent doing everything.

Planning & Reasoning

Task decomposition and re-planning as new information arrives, instead of a fixed script that breaks the moment reality diverges from it.

Tool Calling

Structured function calls that let an agent query systems and take action, with validation on every call.

Retrieval-Augmented Generation (RAG)

Agent responses and decisions grounded in retrieved context from your own data, not the model's training data alone.

Knowledge Bases

Structured, searchable knowledge sources an agent retrieves from, kept current rather than a one-time export.

Enterprise Automation

Agents embedded in existing operational processes, not a separate tool competing with the systems your team already uses.

Workflow Orchestration

Coordination logic that sequences agent actions, tool calls, and human steps into one reliable process.

Human-in-the-loop

Defined checkpoints where a person reviews or approves before an agent's action takes effect.

Agent Memory

Short- and long-term state management so an agent can maintain context across a task or across sessions.

Vector Databases

Purpose-built storage and indexing for the embedding-based retrieval agents depend on.

Monitoring

Visibility into what an agent is actually doing in production — decisions, tool calls, latency, and failures.

Evaluation

Systematic testing against real scenarios, so behavior regressions are caught before users encounter them.

Security

Scoped permissions, input validation, and audit logging built to the same standard as the rest of your systems.
Process

How we work

The same disciplined process behind every engagement, from the first architecture decision to launch.

  1. 01
    Discovery

    Understand the business problem and its real constraints.

    Output:
    Scope and goals document
    Your involvement:
    Initial workshop
  2. 02
    Product definition

    Translate the problem into concrete product requirements.

    Output:
    Feature spec and priorities
    Your involvement:
    Requirements review
  3. 03
    UX/UI design

    Design user flows and interface before development starts.

    Output:
    Wireframes and design system
    Your involvement:
    Design feedback
  4. 04
    Technical architecture

    Define system structure, data flow, and technology stack.

    Output:
    Architecture document
    Your involvement:
    Technical review (optional)
  5. 05
    Iterative development

    Build in short cycles with visible, regular progress.

    Output:
    Regularly shipped working versions
    Your involvement:
    Sprint review participation
  6. 06
    Quality assurance

    Test functionality, performance, and security before release.

    Output:
    Test results and fixes
    Your involvement:
    Acceptance sign-off
  7. 07
    Launch

    Deploy to production with a rollback plan in place.

    Output:
    Product deployed to production
    Your involvement:
    Launch approval
  8. 08
    Continuous improvement

    Monitor, maintain, and evolve the product after launch.

    Output:
    Maintenance and improvement roadmap
    Your involvement:
    Regular check-in meetings
Technology Stack

Built on a modern, production-grade stack

Every technology here is a deliberate choice, not a default.

OpenAI

A model provider for agent reasoning, tool calling, and multimodal tasks.

Anthropic Claude

A model provider used where its reasoning and tool-use behavior fits the task.

Google Gemini

A model provider evaluated alongside others based on the specific agent's requirements.

LangGraph

A framework for building stateful, multi-step agent workflows with explicit control flow.

LangChain

Tooling for retrieval, prompt orchestration, and integration with external systems.

Model Context Protocol

A standardized way for agents to connect to tools and data sources across different systems.

Python

The language of choice for agent orchestration, evaluation pipelines, and data-heavy workloads.

Node.js

A backend runtime well suited to streaming agent responses and orchestrating tool calls.

Next.js

A full-stack framework for agent-powered interfaces that need both a frontend and server-side orchestration.

Laravel

An enterprise backend framework for agents integrated into existing business applications.

PostgreSQL

The relational database of choice, often paired with pgvector for combined relational and vector storage.

Redis

Caching and short-term state storage for agent sessions and tool-call results.

pgvector

Vector similarity search directly inside PostgreSQL, when a separate vector database isn't justified.

Pinecone

A managed vector database for retrieval at production scale.

Docker

Containerized builds for consistent environments across development, staging, and production.

AWS

Cloud infrastructure for applications that need more control than a managed platform alone provides.
Enterprise AI Features

Enterprise AI Features

The architecture decisions that determine whether an AI agent is reliable, safe, and affordable to run in production, not just a working prototype.

Agent Planning

A planning loop that decomposes a goal into steps and adapts as conditions change, designed and tested like any other software logic.

Memory

State that persists across a task or session deliberately, sized to what the agent actually needs rather than growing unbounded.

Tool Calling

Function definitions and execution boundaries designed so an agent can act safely within defined limits.

RAG

A retrieval pipeline built around your actual data, so agent decisions are grounded in current, relevant information.

Context Management

Deliberate control over what an agent sees at each step, so context stays relevant instead of growing until it degrades output quality.

Guardrails

Scope limits, input and output validation, and fallback behavior designed before launch, not added after an incident.

Observability

Full visibility into an agent's decisions, tool calls, and outcomes — the difference between debugging and guessing.

Evaluation

Systematic testing against real scenarios and edge cases, run continuously rather than once before launch.

Caching

Response and retrieval caching that cuts cost and latency for steps that don't need a fresh model call every time.

Prompt Versioning

Prompts and agent instructions tracked and tested like code, so changes are deliberate and reversible.

Cost Optimisation

Model selection, context length, and tool-call frequency tuned deliberately against actual usage patterns.

Security

Scoped credentials, action validation, and audit trails built around the same standards as the rest of the system.
Representative Solutions

Where this technology fits

Reference architectures from our Representative Solutions collection that could plausibly be built on this stack.

AI Operations Platform
Representative Solution

AI Operations Platform

Operations & AI
Customer Support AI
Representative Solution

Customer Support AI

Customer Experience
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