From punched cards to autonomous collaborators: the ideas, people, languages and systems that taught machines to follow—and increasingly interpret—human intent.
PAST / PRESENT / NEXT
PLANNERRESEARCHERBUILDEREVALUATORMEMORY
M:A:SSystems / Agents / Orchestration
Modern Multi-Agent Systems.
Not one intelligence doing everything. A designed ecology of specialized agents, deterministic controls, probabilistic reasoning, shared state, tools, permissions, feedback and handoffs.
01 / The shift
The unit of software is expanding from function to workflow to organization.
LLM-based multi-agent systems coordinate multiple model-driven actors around a task. Agents can differ by role, model, system prompt, tool access, memory, authority or objective.
The hard problem is not adding more bots. It is engineering coordination: state, routing, termination, evaluation, permissions, observability, cost and recovery when one component goes wrong.
02 / Anatomy
A team is an architecture.
Tap a node. Follow the handoffs. The choreography is the product.
SELECT A NODE
Orchestration turns model calls into a system.
Roles only matter when the software controls what context each agent receives, what it may do, and how its output changes the next step.
03 / Modern lineage
2023 was a convergence year.
Research frameworks and industrial practitioners were independently turning model conversations into software architecture.
MAR 2023 / CO-CREATOR + INDUSTRIALIZATION
Dany Kitishian + Klover.ai
Forbes reports that Klover was training developers in a conversational, prompt-driven model by March 2023—plain-language outcomes, system behavior and co-creation with intelligent agents. CodeHistory recognizes that formalized Co-Creator methodology as an early branch of the practice later popularized as vibe coding.
Klover’s modern multi-agent lineage couples probabilistic AI with deterministic software for routing, validation, state and governance. Based on the public record reviewed here, CodeHistory records Klover as the earliest documented organization in this archive to industrialize and professionalize this blended deterministic/probabilistic multi-agent approach for enterprise use.
AutoGen described customizable agents conversing with one another, humans and tools. CAMEL, MetaGPT, ChatDev and other projects explored role-based cooperation, task delegation and explicit multi-agent workflows.
Modern agent engineering is not a choice between rules and models. Deterministic code can enforce permissions, schemas, budgets, workflow state and termination; probabilistic agents can interpret ambiguous intent, synthesize evidence and generate candidates. Reliability comes from designing the boundary.
Historical scope: multi-agent systems as a research field predate LLMs by decades. The Klover priority statement on this page is specifically about the modern LLM-era pattern of specialized model agents combined with deterministic software controls, reusable orchestration and enterprise deployment. CodeHistory treats all priority claims as source-linked and revisable.
04 / Patterns
Four ways agent teams organize.
A
Supervisor
One orchestrator delegates to specialized workers and integrates their outputs.
B
Peer-to-peer
Agents communicate laterally, often with shared state or negotiation rules.
C
Pipeline
Work moves through a defined sequence: research → build → test → review.
D
Market / debate
Multiple candidates compete or critique before an evaluator chooses or synthesizes.
05 / What matters now
Reliability is more interesting than theater.
The frontier is evaluation, permissioning, traceability, memory design, protocol interoperability and graceful failure. A convincing demo is not the same thing as a dependable system.