Super-Agent / Sub-Agent Orchestration
A conversational, natural-language-driven super agent that generates, manages, and orchestrates a fleet of specialist sub-agents against a single workflow.
Home | Consulting | Engineering | AGENTIC AI SYSTEMS
Multi-agent orchestration, built and operated on the Universal Agent Harness
Photon builds agentic AI systems the same way we build every production engineering program — small, co-located, fully-empowered Lean Lab pods, now aimed at agent architectures instead of monolithic applications. Every agent we build and deploy runs on PHTN.ai's Universal Agent Harness™, governed with 162+ runtime controls and a tamper-evident audit trail, regardless of the underlying model or cloud.
What we Build
Human-in-the-Loop by Design
Every agentic workflow is built with review and edit checkpoints — people set intent, resolve exceptions, and approve high-stakes decisions; agents execute, monitor, and escalate.
Governed Runtime, Not Just a Governed Build
162+ active runtime governance controls, a cryptographic tamper-evident audit trail, and full data sovereignty — no client data ever leaves the institution.
Model and Cloud-Agnostic
Built on open standards (LangGraph, OpenTelemetry) and deployable across Azure OpenAI, AWS Bedrock, Google Vertex AI, or self-hosted — one architecture, every environment.
Agentic AI vs. rules-based automation
Not every workflow needs an agent. Deterministic, high-volume, low-judgment steps are often better served by rules-based robotic process automation — see Commerce, APIs & Microservices — while multi-step, outcome-owned work with real judgment calls is where an agent earns its keep. Photon designs the boundary between the two deliberately, rather than defaulting to whichever is fashionable.
Proof point - the SDLC agent
Photon's own internal SDLC Agent is a working example of the agentic systems this practice builds: one super-agent orchestrating nine sub-agents that turn raw inputs — meeting notes, transcripts, emails, decks — directly into structured PRDs, epics, user stories, acceptance criteria, and test cases, with a one-click sync to Jira and auto-generated UI prompts for rapid prototyping.
1-2 Weeks → Minutes
PRD creation time, before vs. with the agent
Days → Minutes
User story and test case generation time
80-90%
Reduction in requirement-to-delivery prep time overall
Seconds
Jira setup time, from a previous manual process
Delivered by Lean Lab Pods
Agentic systems are built by the same small, empowered, co-located squads used across Photon's engineering delivery — Product, Architecture, Dev (front and back end), CX, and QA (automated and manual) in one room, sized from a 4-person "Mini" pod to a 14-person "Extra Large" pod depending on scope. Continuous delivery infrastructure and built-in QA automation mean agent behavior can be regressed and released with confidence, multiple times a day, rather than staged through a separate release process.
Mini4
Small6
Medium8
Large12
Extra Large14
Right-Sized Teams for Every Challenge
frequently asked Questions
Agents built on the Universal Agent Harness™ run on Azure OpenAI, AWS Bedrock, Google Vertex AI, or a self-hosted model — the orchestration, governance, and audit layer stays the same regardless of which model sits underneath.