IT & Architecture Intern - IT & Architecture Brain

The intern will design and build a proof-of-concept “IT & Architecture Brain,” structured not as one monolithic assistant but a set of specialized agents, each responsible for one activity, each accumulating its own working context and knowledge over time, all drawing on a shared transverse knowledge layer so they stay consistent with one another.

• Architecture knowledge agent — answers “what exists, why was it built this way, and how does it connect” by retrieving and reasoning over the documentations, ADRs.

• Change impact agent — reasons about blast radius before a change (“if we deprecate this API/service, what breaks and who owns it”), useful for architecture and change advisory boards.

• Incident & runbook agent — holds persistent memory across past incidents and postmortems, surfacing “this looks like the outage from [date], here’s what fixed it” instead of starting from zero each time.

• Governance & compliance agent — checks new designs/ADRs against existing standards, reference architectures, and prior decisions, flagging inconsistencies or duplicated capabilities across teams.

  Rather than four disconnected tools, the goal is to demonstrate that one well-structured mesh, sitting on one shared knowledge layer, can power all four agents.

  1)Discovery : Review of enterprise brain tooling approaches; inventory of available sources; scope confirmation

  2)Shared layer & mesh design : Define the shared data model (systems, dependencies, decisions, incidents); define each agent’s scope, context, and memory model; sketch the harness (routing, handoff, access control); design evaluation metrics

  3)Core build  EA knowledge agent : Ingest system catalog, ADRs ; build retrieval + reasoning for agent 1 ;  build a minimal harness (single-agent routing, logging)

  4)Mesh extension : Add one or two more agents (change impact, incident memory, or governance); extend the harness to multi-agent routing and handoff; wire agents to the shared layer and their own memory

  5)Reliability & evaluation : Consistency checks across agents, access control, audit logging, fallback/escalation behavior, benchmark against stateless baseline, validation with architects/engineers

• Documentation : Final report, demo, recommendations for scaling the mesh to more agents/teams

Required experience & competencies

• Programming skills (Python preferred)

• Familiarity with LLMs and how they’re used 

• Basic understanding of information retrieval concepts (embeddings, vector search)

• Interest in software/enterprise architecture concepts (system design, dependencies, documentation practices)

• Comfort working with ambiguous, evolving requirements and iterating quickly

Not required but nice to have :

• Experience with RAG frameworks (LangChain, or similar)

• Exposure to graph databases or knowledge graphs (Neo4j, etc.)

• Familiarity with multi-agent frameworks, orchestration patterns

• Interest in distributed systems reliability concepts (circuit breakers, fallback, observability) applied to AI agents

• A working prototype of the agent mesh (codebase + demo): at least two agents, a shared knowledge layer, and a harness coordinating them

• An architecture document describing the shared data model, each agent’s scope/context/memory, and the harness’s routing and access-control logic

• An evaluation report covering per-agent accuracy 

• A final presentation with recommendations on extending the mesh to additional agents, data sources, or teams