Axioprax AI Lab
Technical experimentation and applied research. Where we push the boundaries of what AI systems can do in production environments — and build the engineering depth that informs every client engagement.
Applied Research, Not Marketing Demonstrations
The Axioprax AI Lab is where we test approaches before recommending them to clients. Experiments address specific engineering questions: how does this agent topology behave under load, how reliable is this extraction pattern, what does MCP server design actually look like in a production codebase.
Results — both positive and negative — inform the engineering decisions we make in client engagements. The lab exists because credible AI engineering requires hands-on experience with the constraints of real systems.
Current and Completed Work
Each experiment addresses a specific technical question relevant to production AI system design.
Multi-Agent Research Pipeline
A pipeline architecture where specialised agents handle distinct phases of a research task — planning, retrieval, synthesis and formatting — coordinated by an orchestrator agent. Explores agent handoff, context passing and result aggregation across agent boundaries.
Browser-Capable Research Agent
An agent equipped with browser tools that autonomously navigates web sources, follows relevant links, extracts structured information and produces formatted research output. Experiments with tool selection, navigation strategy and extraction reliability.
MCP Server Integration Patterns
Systematic exploration of Model Context Protocol server integration patterns — how tools are exposed to AI agents, tool selection behaviour under different schema designs and MCP server architecture for production deployments.
Document Agent with Structured Extraction
An agent that reads documents of varying structure and extracts predefined schemas using function calling and constrained output. Tests extraction accuracy, handling of ambiguous fields and fallback behaviour when fields are absent.
AI Code Review System
A code review agent configured against custom coding guidelines, security patterns and architectural rules. Runs on pull request events, produces structured review comments and integrates with GitHub review workflows without blocking CI.
Agent Observability Framework
Infrastructure for understanding what AI agents are doing in production: trace logging, token consumption tracking, tool call recording, error classification and cost attribution by workflow. Feeds into operational decision-making for agent systems.
Lab Work Informs Client Engagements
When we recommend an agent architecture or integration pattern to a client, we have tested it. The lab is where those tests happen — so clients do not pay for our learning curve.
Tested Patterns
Architectures we recommend have been tried in the lab before appearing in a client system.
Known Failure Modes
We document where approaches break down — reliability thresholds, latency characteristics and cost at scale.
Engineering Depth
The lab builds the hands-on understanding that separates AI engineering from AI experimentation.
Interested in the Engineering Behind Our Work?
Talk to us about your technical requirements. We bring the same engineering rigour from the lab to every client engagement.