Architecture

glossary

Overview

Software architecture is the discipline concerned with the high-level structuring of software systems. It defines the fundamental organization of a system, embodied in its components, their relationships to each other and the environment, and the principles governing its design and evolution. Architecture is not merely about choosing a framework or drawing box-and-arrow diagrams; it is about making the decisions that are costly to change later and that profoundly affect every aspect of a system's lifetime -- from development velocity and team organization to operational resilience and long-term maintainability.

Within the Prismatic Platform, architecture is elevated to a first-class engineering concern. With 115 umbrella applications, 530+ autonomous agents, and a codebase exceeding 2.8 million lines of code, architectural discipline is not optional -- it is the difference between a coherent system and an unmaintainable monolith. The platform's architecture leverages Elixir's OTP framework, supervision trees, and the BEAM virtual machine to achieve fault tolerance, horizontal scalability, and real-time responsiveness that would be extraordinarily difficult to replicate in conventional technology stacks.

Definition and Etymology

The term "architecture" derives from the Greek arkhitekton (master builder), combining arkhi- (chief) and tekton (builder or carpenter). In the physical world, architecture has governed the design of structures for millennia -- from the load-bearing calculations of Roman aqueducts to the material science of modern skyscrapers. The analogy to software was first drawn explicitly in the late 1960s, when Edsger Dijkstra and others began recognizing that large software systems required the same kind of deliberate structural planning that physical buildings demand.

The IEEE Standard 1471-2000 defines software architecture as "the fundamental organization of a system embodied in its components, their relationships to each other, and to the environment, and the principles guiding its design and evolution." Martin Fowler offers a more pragmatic definition: architecture is "the shared understanding that the expert developers have of the system design" combined with "the decisions that are hard to change." Both definitions capture an essential truth -- architecture is about the decisions that matter most because they constrain everything that follows.

The field matured through several phases: the structured programming era of the 1970s, the object-oriented revolution of the 1980s and 1990s, the service-oriented architecture (SOA) movement of the 2000s, and the microservices and cloud-native paradigm of the 2010s and 2020s. Each phase brought new patterns, new trade-offs, and new understanding of what constitutes good structural design.

Historical Context

The history of software architecture mirrors the history of software complexity. In the 1950s and 1960s, programs were small enough that a single developer could hold the entire design in their head. The software crisis of the late 1960s -- famously articulated at the 1968 NATO Software Engineering Conference -- revealed that as systems grew, ad hoc approaches to structure led to catastrophic failures in cost, schedule, and reliability.

The 1970s introduced structured programming (Dijkstra), information hiding (Parnas), and the earliest formalization of modular design. David Parnas's 1972 paper "On the Criteria To Be Used in Decomposing Systems into Modules" remains one of the most influential works in software engineering, establishing that module boundaries should be drawn around likely changes rather than around processing steps.

The 1990s saw the emergence of architecture as a distinct discipline. The landmark book Software Architecture: Perspectives on an Emerging Discipline by Shaw and Garlan (1996) catalogued architectural styles including pipes-and-filters, layered systems, event-driven architectures, and repository patterns. The Gang of Four's Design Patterns (1994) addressed the micro-level of architectural decisions, while architectural description languages (ADLs) attempted to formalize the macro-level.

The 2000s brought service-oriented architecture and enterprise integration patterns, while the 2010s saw the explosion of microservices, container orchestration, and cloud-native design. Today, architecture increasingly encompasses distributed systems concerns, observability, platform engineering, and the organizational implications captured by Conway's Law.

Core Architectural Patterns

Monolithic Architecture

The monolithic pattern deploys the entire application as a single unit. All components share the same process space, memory, and deployment lifecycle. Monoliths are simpler to develop, test, and deploy for small to medium systems, but they create coupling that makes scaling, independent deployment, and team autonomy progressively harder as the system grows.

Microservices Architecture

Microservices decompose a system into independently deployable services, each owning its own data and communicating through well-defined APIs or message protocols. This pattern enables independent scaling, polyglot technology choices, and team autonomy, but introduces distributed systems complexity including network partitions, data consistency challenges, and operational overhead.

Event-Driven Architecture

Event-driven architecture organizes systems around the production, detection, and consumption of events. Components communicate by emitting and subscribing to events rather than making direct calls. This pattern excels at decoupling, temporal flexibility, and audit trails. It is closely related to event sourcing and CQRS, which separate the write model from the read model.

Hexagonal Architecture (Ports and Adapters)

Proposed by Alistair Cockburn, hexagonal architecture places domain logic at the center, surrounded by ports (interfaces defining how the domain interacts with the outside world) and adapters (implementations of those interfaces for specific technologies). This pattern enforces a strict dependency rule: external concerns depend on the domain, never the reverse.

Clean Architecture

Robert C. Martin's clean architecture extends hexagonal architecture into concentric rings: entities (enterprise business rules), use cases (application business rules), interface adapters, and frameworks/drivers. The dependency rule mandates that source code dependencies can only point inward, ensuring that business logic remains independent of databases, UI frameworks, and external agencies.

CQRS (Command Query Responsibility Segregation)

CQRS separates the read and write sides of a system into distinct models. Commands mutate state through a write model optimized for consistency and validation, while queries read from a read model optimized for performance and the specific needs of consumers. This separation enables independent scaling and optimization of each path.

Technical Deep Dive: Prismatic Platform Architecture

The Prismatic Platform represents a distinctive architectural approach that combines the best aspects of monolithic coherence with microservices modularity. It achieves this through Elixir's umbrella application pattern -- a compile-time monolith that functions as a runtime distributed system.

Umbrella Application Structure

The platform comprises 115 umbrella applications, each with its own mix.exs, test suite, and clearly defined responsibility. This structure provides the compile-time benefits of a monolith (shared type checking, cross-application refactoring, unified dependency management) with the organizational benefits of microservices (clear boundaries, independent testing, domain-specific ownership).

apps/
  prismatic/                     # Core coordination and facade
  prismatic_web/                 # Phoenix LiveView dashboards (port 4000)
  prismatic_api/                 # Auto-introspecting REST API (port 4004)
  prismatic_agents/              # 530+ agent runtime
  prismatic_storage_core/        # Storage behaviours and protocols
  prismatic_storage_ets/         # In-memory ETS adapter
  prismatic_storage_ecto/        # PostgreSQL adapter via Ecto
  prismatic_storage_meilisearch/ # Full-text search adapter
  prismatic_storage_kuzu/        # Graph database adapter (KuzuDB)
  prismatic_perimeter/           # EASM and security ratings
  prismatic_supervisor/          # Compositional supervision
  prismatic_safety/              # Quality floor guardian
  prismatic_claude/              # LLM session integration
  prismatic_credo/               # Custom Credo checks
  ...                            # 101 more specialized applications

Architectural Principles

The platform's architecture is governed by several non-negotiable principles:

  1. Process per entity: Every stateful entity (agent, connection, session, pool) is its own OTP process with its own heap, mailbox, and failure domain.

  2. Supervision trees over exception handling: Instead of try/catch at every call site, define supervision strategies that automatically restart failed processes.

  3. Behaviours as contracts: All inter-component communication is defined through Elixir behaviours (compile-time verified contracts), never through ad-hoc function calls.

  4. Umbrella isolation: Each of the 115 applications has its own supervision tree, dependency list, and compilation unit. Cross-application dependencies are explicit and acyclic.

  5. Functional core, imperative shell: Business logic is implemented as pure functions. Side effects (database writes, HTTP calls, process messaging) happen only at the edges.

  6. Configuration over convention: Runtime behaviour is driven by configuration (config/*.exs), not hardcoded values. Different environments (dev, test, staging, prod) can radically alter system behaviour.

OTP Supervision Trees

At the heart of the platform's resilience lies the OTP supervision tree pattern. Every stateful component runs as a supervised process. When a process crashes, its supervisor restarts it according to a defined strategy, isolating failures and preventing cascading collapse.

defmodule PrismaticPerimeter.Application do
  @moduledoc """
  OTP Application for the Prismatic Perimeter EASM module.
  Defines the supervision tree for security rating, asset discovery,
  and compliance assessment processes.
  """

  use Application

  @impl true
  def start(_type, _args) do
    children = [
      PrismaticPerimeter.Repo,
      {PrismaticPerimeter.AssetDiscovery.Supervisor, []},
      {PrismaticPerimeter.SecurityRating.Calculator, []},
      {PrismaticPerimeter.Compliance.Assessor, []},
      {PrismaticPerimeter.Dashboard.PubSub, []},
      {PrismaticPerimeter.Telemetry, []}
    ]

    opts = [strategy: :one_for_one, name: PrismaticPerimeter.Supervisor]
    Supervisor.start_link(children, opts)
  end
end

The root supervisor organizes child applications into failure-isolated domains:

defmodule Prismatic.RootSupervisor do
  @moduledoc """
  Root supervisor for the Prismatic Platform.
  Organizes child applications into failure-isolated domains.
  """

  use Supervisor

  @impl true
  def init(_init_arg) do
    children = [
      # Infrastructure domain - must start first
      {Prismatic.Infrastructure.Supervisor, []},

      # Storage domain - depends on infrastructure
      {Prismatic.Storage.Supervisor, []},

      # Intelligence domain - depends on storage
      {Prismatic.Intelligence.Supervisor, []},

      # Web domain - depends on storage and intelligence
      {Prismatic.Web.Supervisor, []},

      # Evolution domain - depends on everything
      {Prismatic.Evolution.Supervisor, []}
    ]

    Supervisor.init(children, strategy: :rest_for_one)
  end
end

The :rest_for_one strategy ensures that if the storage domain crashes, the intelligence, web, and evolution domains (which depend on storage) are also restarted, but infrastructure remains running. The supervision strategy choices carry deep architectural significance: :one_for_one restarts only the failed child (suitable for independent services), :one_for_all restarts all children when any fails (appropriate when children share critical state), and :rest_for_one restarts the failed child and all children started after it (modeling dependency chains).

Actor Model and Process Architecture

Every significant entity in the Prismatic Platform has its own lightweight BEAM process. Agents, storage adapters, health monitors, and session managers all run as independent processes communicating exclusively through message passing. This eliminates shared mutable state, prevents race conditions without locks, and enables the platform to exploit all available CPU cores transparently.

defmodule PrismaticAgents.AgentProcess do
  use GenServer

  defstruct [:agent_id, :config, :state, :metrics]

  # Client API
  def start_link(opts) do
    agent_id = Keyword.fetch!(opts, :agent_id)
    GenServer.start_link(__MODULE__, opts, name: via_tuple(agent_id))
  end

  def execute(agent_id, command) do
    GenServer.call(via_tuple(agent_id), {:execute, command}, :timer.seconds(30))
  end

  # Server callbacks
  @impl true
  def init(opts) do
    agent_id = Keyword.fetch!(opts, :agent_id)
    config = Keyword.fetch!(opts, :config)

    state = %__MODULE__{
      agent_id: agent_id,
      config: config,
      state: :initialized,
      metrics: %{executions: 0, errors: 0, last_active: DateTime.utc_now()}
    }

    {:ok, state, {:continue, :post_init}}
  end

  @impl true
  def handle_continue(:post_init, state) do
    :telemetry.execute(
      [:prismatic, :agent, :started],
      %{count: 1},
      %{agent_id: state.agent_id}
    )
    {:noreply, %{state | state: :ready}}
  end

  @impl true
  def handle_call({:execute, command}, _from, state) do
    case execute_command(state, command) do
      {:ok, result, new_state} ->
        updated = %{new_state | metrics: update_metrics(new_state.metrics, :success)}
        {:reply, {:ok, result}, updated}

      {:error, reason} ->
        updated = %{state | metrics: update_metrics(state.metrics, :error)}
        {:reply, {:error, reason}, updated}
    end
  end

  defp via_tuple(agent_id),
    do: {:via, Registry, {PrismaticAgents.Registry, agent_id}}

  defp execute_command(state, command),
    do: {:ok, %{executed: command}, state}

  defp update_metrics(metrics, :success),
    do: %{metrics | executions: metrics.executions + 1, last_active: DateTime.utc_now()}

  defp update_metrics(metrics, :error),
    do: %{metrics | executions: metrics.executions + 1, errors: metrics.errors + 1, last_active: DateTime.utc_now()}
end

Trait-Based Storage Architecture

The platform uses a trait-based approach (Elixir behaviours as traits) to define contracts between architectural layers:

defmodule PrismaticStorageCore.Traits.Queryable do
  @moduledoc """
  Trait for storage backends that support structured queries.
  """

  @callback query(queryable :: term(), filters :: keyword()) ::
              {:ok, [map()]} | {:error, term()}

  @callback aggregate(queryable :: term(), aggregation :: atom(), field :: atom()) ::
              {:ok, term()} | {:error, term()}

  @callback paginate(queryable :: term(), page :: pos_integer(), per_page :: pos_integer()) ::
              {:ok, %{data: [map()], total: non_neg_integer(), page: pos_integer()}}
              | {:error, term()}
end

Storage backends compose traits to declare their capabilities, and concrete adapters implement the relevant behaviours. This allows the same business logic to run against PostgreSQL in production, ETS in development, and mock adapters in tests, without any code changes.

Cross-Application Communication

Applications within the umbrella communicate through well-defined facade modules, never through internal module access:

defmodule PrismaticPerimeter do
  @moduledoc """
  Public facade for the Prismatic Perimeter EASM module.
  All external access to Perimeter functionality goes through this module.
  """

  @spec discover(binary(), keyword()) :: {:ok, map()} | {:error, term()}
  def discover(domain, opts \\ []) do
    PrismaticPerimeter.AssetDiscovery.discover(domain, opts)
  end

  @spec security_rating(binary(), keyword()) :: {:ok, map()} | {:error, term()}
  def security_rating(domain, opts \\ []) do
    PrismaticPerimeter.SecurityRating.Calculator.calculate(domain, opts)
  end

  @spec assess_compliance(binary(), [atom()], keyword()) :: {:ok, map()} | {:error, term()}
  def assess_compliance(domain, frameworks, opts \\ []) do
    PrismaticPerimeter.Compliance.Assessor.assess(domain, frameworks, opts)
  end
end

Architectural Quality Attributes

Quality attributes (also called non-functional requirements or "-ilities") are the properties that determine how well a system fulfills its functional requirements. Architecture is fundamentally about optimizing the trade-offs between these attributes.

Scalability

Scalability measures a system's ability to handle growing load. Vertical scaling adds resources to existing nodes; horizontal scaling adds more nodes. The BEAM VM's lightweight processes and distributed Erlang capabilities make horizontal scaling natural -- the Prismatic Platform can distribute its 530+ agents across multiple nodes with minimal code changes.

Reliability and Fault Tolerance

Fault tolerance is the ability to continue operating correctly in the presence of failures. OTP's "let it crash" philosophy treats failures as expected events rather than exceptional conditions. Supervision trees, circuit breakers, and bulkhead patterns ensure that individual component failures do not cascade into system-wide outages.

Maintainability

Maintainability encompasses how easily a system can be modified, extended, and understood. The umbrella application pattern directly supports maintainability by enforcing clear module boundaries. Each of the 115 apps has its own CLAUDE.md documentation, quality DNA state, and test suite, making it possible for teams to work on individual domains without deep knowledge of the entire codebase.

Security

Architectural security involves defense in depth, least privilege, and secure-by-default design. The Prismatic Platform's color-team security operations (Red, Blue, Purple, White, Gray, Black teams) provide continuous adversarial testing of architectural assumptions. The 13-layer Trinity Gate ensures that no claim or decision passes without structural, logical, and formal verification.

Performance

Performance is not an afterthought but an architectural concern. The platform enforces hard limits: page loads under 250ms, server-side renders under 100ms, LiveView mounts under 150ms. O(1) pattern detection algorithms (90-250x faster than naive approaches) and Git tree optimization (~100x faster file exploration) demonstrate that performance is designed in, not bolted on.

Domain-Driven Design and Bounded Contexts

Domain-Driven Design (DDD), articulated by Eric Evans in 2003, provides the strategic design tools for organizing large systems around business domains. The central concept is the bounded context -- a linguistic and conceptual boundary within which a particular domain model applies consistently.

The Prismatic Platform's umbrella structure maps naturally to bounded contexts. Each application represents a distinct domain: prismatic_perimeter owns the External Attack Surface Management domain, prismatic_agents owns the agent lifecycle domain, prismatic_storage_core defines the storage abstraction domain. Communication between contexts occurs through well-defined interfaces -- Elixir behaviours and protocols -- rather than direct module coupling.

DDD also introduces the concept of context mapping, which describes the relationships between bounded contexts. These relationships include Shared Kernel (shared code between contexts), Customer-Supplier (upstream context provides what downstream needs), Anti-Corruption Layer (translation layer protecting a context from external model pollution), and Published Language (a well-documented interchange format).

The platform's AIAD (AI Agent Directive) standard serves as a published language -- a formal specification that enables 530+ agents across 16 domains to communicate without requiring each agent to understand the internal models of every other domain.

Architecture Decision Records

Architecture Decision Records (ADRs) capture the rationale behind significant architectural decisions. Popularized by Michael Nygard, each ADR documents the context (what forces are at play), the decision itself, the status (proposed, accepted, deprecated, superseded), and the consequences (both positive and negative).

ADRs prevent architectural knowledge loss. When a new team member asks "why do we use ETS instead of Redis for the agent registry?", the answer should be in an ADR, not in someone's memory. The format is deliberately lightweight to encourage adoption:

# ADR-042: ETS for Agent Registry Storage

## Status
Accepted

## Context
The agent registry requires sub-millisecond lookups for 530+ agents.
External stores (Redis, PostgreSQL) add network latency and failure modes.

## Decision
Use ETS (Erlang Term Storage) as the primary agent registry backend,
with Horde as the distributed backend for production clusters.

## Consequences
- Positive: Sub-microsecond lookups, no external dependencies
- Positive: Automatic garbage collection when owning process dies
- Negative: Data lost on node restart (mitigated by Horde replication)
- Negative: Limited to ~2GB per table on 64-bit systems

Architecture Comparison

ApproachScalabilityFault ToleranceComplexityDeploy UnitPrismatic Fit
MonolithVerticalShared fateLowSingleToo rigid for 115 apps
MicroservicesHorizontalService-levelVery highPer-serviceToo much operational overhead
Elixir UmbrellaBothProcess-levelModerateSingle releaseOptimal balance
Modular MonolithVerticalModule-levelModerateSingleMissing OTP supervision
ServerlessAutoFunction-levelLow per functionPer-functionMissing long-lived processes

The Elixir umbrella architecture provides the modularity of microservices (115 independent applications with explicit boundaries) with the operational simplicity of a monolith (single deployment, single release, shared VM). OTP supervision adds fault tolerance that surpasses what microservices achieve through external orchestrators like Kubernetes.

Anti-Patterns

Big Ball of Mud

The most common architectural failure mode. A system without discernible structure where every component knows about and depends on every other component. Changes ripple unpredictably. The Prismatic Platform prevents this through strict umbrella boundaries, dependency analysis (mix supervisor deps --cycles), and the forbidden patterns enforcement system that blocks architectural violations at commit time.

God Object

A single module that accumulates responsibility until it becomes the center of the universe. In Elixir terms, this would be a GenServer with hundreds of handle_call clauses. The platform's naming standards explicitly forbid vague names like "Manager", "Handler", or "Utils" that tend to attract unrelated responsibility.

Golden Hammer

Using a familiar technology or pattern for every problem regardless of fit. The platform guards against this by maintaining multiple storage adapters (ETS, Ecto/PostgreSQL, Meilisearch, KuzuDB) and selecting each based on the specific data model and access pattern requirements of the domain.

Distributed Monolith

The worst of both worlds: a system decomposed into services that must be deployed together and share a database. The umbrella pattern avoids this by design -- applications can have explicit compile-time dependencies without introducing the operational complexity of network boundaries where they add no value.

Circular Dependencies

Application A depends on B, B depends on A. This creates fragile coupling that prevents independent evolution. The platform extracts shared logic into _core applications that both depend on, and circular dependencies are detected at compile time and blocked by the mix quality.gates check.

Supervision Tree Neglect

Using default supervision strategies without thinking about failure domains. A crash in asset discovery should not restart the compliance assessor. Each supervision tree is explicitly designed with documented rationale for strategy selection.

Elixir/OTP Architectural Advantages

Elixir and OTP provide architectural capabilities that are difficult or impossible to achieve in mainstream technology stacks:

Fault Isolation: Every process has its own heap. A crash in one process cannot corrupt another process's memory. This is a stronger isolation guarantee than threads in any conventional language.

Hot Code Reloading: OTP supports upgrading running code without stopping the system. While not commonly used in cloud-native deployments (rolling restarts are simpler), this capability reflects the deep architectural thinking in the BEAM platform.

Transparent Distribution: Erlang distribution allows processes on different nodes to communicate using the same message-passing primitives as local processes. Scaling from a single node to a cluster requires minimal code changes.

Backpressure: GenStage and Broadway provide built-in backpressure mechanisms for data processing pipelines, preventing fast producers from overwhelming slow consumers.

Observability: The BEAM VM provides introspection capabilities (process inspection, message queue monitoring, memory analysis) that are unavailable in other runtimes, making architectural problems visible before they become incidents.

Meta-Rule: If the same solution could be written identically in Node.js, it is WRONG. Elixir architecture should leverage OTP's unique strengths -- supervision, process isolation, message passing, distribution -- rather than treating the BEAM as just another runtime.

Best Practices

  1. Define application boundaries by domain, not by technology. prismatic_perimeter contains security rating logic, not "all Phoenix controllers" or "all Ecto schemas."

  2. Use facade modules as the only public API. Every application exposes a single top-level module (e.g., PrismaticPerimeter) as its public interface. Internal modules are implementation details.

  3. Enforce acyclic dependencies. Use mix quality.gates to detect and prevent circular dependencies between umbrella applications.

  4. Design supervision trees explicitly. Document the supervision tree before writing code. The tree defines failure domains, restart strategies, and startup order.

  5. Separate functional core from side effects. Business logic in pure functions, side effects (database, HTTP, process messaging) at the edges.

  6. Use behaviours for all cross-application contracts. Never call internal modules of another application. Define behaviours in _core applications and implement them in concrete applications.

  7. Measure before optimizing. Use telemetry and benchmarks to identify actual bottlenecks rather than optimizing based on intuition.

  8. Align architecture with team structure. Conway's Law is real: the architecture will eventually mirror the communication structure of the organization. Design both intentionally.

  9. Version architectural decisions. Use Architecture Decision Records (ADRs) to document why specific architectural choices were made.

  10. Test at the right level. Unit tests for pure functions, integration tests for adapter implementations, end-to-end tests for cross-application workflows.

Use Cases

EASM Security Platform

The Prismatic Perimeter architecture demonstrates how complex security assessment workflows are decomposed into supervised processes: asset discovery, security rating calculation, compliance assessment, and real-time dashboard updates all run as independent supervised processes within a single application.

Multi-Backend Storage

The trait-based storage architecture allows the same business logic to run against PostgreSQL in production, ETS in development, and mock adapters in tests, without any code changes.

Auto-Introspecting API Gateway

The Prismatic API application demonstrates the auto-introspecting architecture: at boot time, it scans all Prismatic* facade modules, discovers their public functions and typespecs, and automatically generates a fully documented OpenAPI 3.0 REST API with zero manual configuration.

Agent Ecosystem Management

The 530+ AIAD agents are organized into domain-specific applications with independent supervision trees, enabling fault-isolated agent execution across security, quality, OSINT, and evolution domains.

TechnologyRelationship to Architecture
OTPProvides the foundational framework for fault-tolerant architecture in Elixir
Supervision TreesThe primary mechanism for organizing process hierarchies
MicroservicesAn architectural pattern for decomposing systems into independent services
Domain-Driven DesignStrategic design methodology for aligning architecture with business domains
CQRSPattern for separating read and write architectural concerns
Event SourcingPattern for persisting state as a sequence of immutable events
ScalabilityQuality attribute that architecture must explicitly support
Fault ToleranceThe ability to continue operating despite component failures
TelemetryObservability infrastructure for monitoring architectural health
MonitoringOperational practice for detecting architectural degradation

Future Directions

Software architecture continues to evolve in response to new challenges:

AI-Native Architecture: As AI agents become first-class system components (the Prismatic Platform already runs 530+ agents), architectures must accommodate non-deterministic components, model versioning, and inference latency patterns that differ fundamentally from traditional request-response services.

Platform Engineering: The shift from DevOps to platform engineering treats internal developer platforms as products. Architecture increasingly includes the developer experience -- how easy it is to create, test, deploy, and observe new components within the platform.

Sustainability: Computational efficiency is becoming an architectural concern beyond performance. Energy-efficient architectures that minimize unnecessary computation, reduce data movement, and optimize resource utilization represent a growing area of architectural thinking.

Formal Verification Integration: The Prismatic Platform's Trinity Gate (structural, logical, and formal verification) points toward a future where architectural decisions are not just documented but formally proven to maintain desired properties as the system evolves.

Self-Evolving Architecture: The platform has evolved through 19 generations, from a simple monolith (Gen 1-3, quality 40/100) to a self-evolving umbrella with formal verification (Gen 17-19, quality 100/100, 115 applications). The quality floor guardian, predictive pre-commit hooks, and autonomous evolution system point toward architectures that adapt and improve without human intervention.

See Also

  • Supervision Tree -- OTP's hierarchical process management pattern
  • Microservices -- Independent service decomposition pattern
  • Domain-Driven Design -- Strategic design methodology
  • Fault Tolerance -- Resilience through failure isolation
  • OTP -- Open Telecom Platform framework
  • Scalability -- System capacity growth capabilities
  • CQRS -- Command Query Responsibility Segregation
  • Event Sourcing -- Immutable event persistence pattern
  • Telemetry -- Runtime observability and metrics collection
  • Monitoring -- Operational health tracking and alerting
  • Layered Architecture -- Architectural style organizing code into horizontal layers
  • Elixir -- The programming language enabling the platform's architectural patterns

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Created by Tomas Korcak (korczis) | Open Source under GHL

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