Agent Skills: Amplifier Design Philosophy

Amplifier Design Philosophy

Purpose: Complete design philosophy for Amplifier - principles, patterns, and decision frameworks that guide all development.

Use: When requirements are unclear, use this document and the codebase to make correct, aligned decisions.

Core Principles

1. Mechanism, Not Policy

• Kernel provides capabilities and stable contracts
• Modules decide behavior (which provider, how to orchestrate, what to log)
• If two teams might want different behavior → module, not kernel

2. Ruthless Simplicity

• KISS taken to heart: as simple as possible, but no simpler
• Minimize abstractions - every layer must justify existence
• Start minimal, grow as needed (avoid future-proofing)
• Code you don't write has no bugs

3. Small, Stable, Boring Kernel

• Kernel changes rarely, maintains backward compatibility always
• Easy to reason about by single maintainer
• Favor deletion over accretion in kernel
• Innovation happens at edges (modules)

4. Modular Design (Bricks & Studs)

• Each module = self-contained "brick" with clear responsibility
• Interfaces = "studs" that allow independent regeneration
• Prefer regeneration over editing (rebuild from spec, don't line-edit)
• Stable contracts enable parallel development

5. Event-First Observability

• If it's important → emit a canonical event
• If it's not observable → it didn't happen
• One JSONL stream = single source of truth
• Hooks observe without blocking

6. Text-First, Inspectable

• Human-readable, diffable, versionable representations
• JSON schemas for validation
• No hidden state, no magic globals
• Explicit > implicit

The Linux Kernel Decision Framework

Use Linux kernel as a metaphor when decisions are unclear.

Metaphor Mapping

| Linux Concept | Amplifier Analog | Decision Guidance | |---------------|------------------|-------------------| | Ring 0 kernel | amplifier-core | Export mechanisms (mount, emit), never policy. Keep tiny & boring. | | Syscalls | Session operations | Few and sharp: create_session(), mount(), emit(). Stable ABI. | | Loadable drivers | Modules (providers, tools, hooks, orchestrators) | Compete at edges; comply with protocols; regeneratable. | | Signals/Netlink | Event bus / hooks | Kernel emits lifecycle events; hooks observe; non-blocking. | | /proc & dmesg | Unified JSONL log | One canonical stream; redaction before logging. | | Capabilities/LSM | Approval & capability checks | Least privilege; deny-by-default; policy at edges. | | Scheduler | Orchestrator modules | Swap strategies by replacing module, not changing kernel. | | VM/Memory | Context manager | Deterministic compaction; emit context:* events. |

Decision Playbook

When requirements are vague:

1. Is this kernel work?

   • If it selects, optimizes, formats, routes, plans → module (policy)
   • Kernel only adds mechanisms many policies could use
   • Litmus test: Could two teams want different behavior? → Module

2. Do we have two implementations?

   • Prototype at edges first
   • Extract to kernel only after ≥2 modules converge on the need

3. Prefer regeneration

   • Keep contracts stable
   • Regenerate modules to new spec (don't line-edit)

4. Event-first

   • Important actions → emit canonical event
   • Hooks observe without blocking

5. Text-first

   • All diagnostics → JSONL
   • External views derive from canonical stream

6. Ruthless simplicity

   • Fewer moving parts wins
   • Clearer failure modes wins

Kernel vs Module Boundaries

Kernel Responsibilities (Mechanisms)

What kernel does:

• Stable contracts (protocols, schemas)
• Module loading/unloading (mount/unmount)
• Event dispatch (emit lifecycle events)
• Capability checks (enforcement mechanism)
• Minimal context plumbing (session_id, request_id)

What kernel NEVER does:

• Select providers or models (policy)
• Decide orchestration strategy (policy)
• Choose tool behavior (policy)
• Format output or pick logging destination (policy)
• Make product decisions (policy)

Module Responsibilities (Policies)

Providers: Which model, what parameters Tools: How to execute capabilities Orchestrators: Execution strategy (basic, streaming, planning) Hooks: What to log, where to log, what to redact Context: Compaction strategy, summarization approach Agents: Configuration overlays for sub-sessions

Evolution Without Breaking Modules

Additive evolution:

• Add optional capabilities (feature negotiation)
• Extend schemas with optional fields
• Provide deprecation windows for removals

Two-implementation rule:

• Need from ≥2 independent modules before adding to kernel
• Proves the mechanism is actually general

Backward compatibility is sacred:

• Kernel changes must not break existing modules
• Clear deprecation notices + dual-path support
• Long sunset periods

Module Design: Bricks & Studs

The LEGO Model

Think of software as LEGO bricks:

Brick = Self-contained module with clear responsibility Stud = Interface/protocol where bricks connect Blueprint = Specification (docs define target state) Builder = AI generates code from spec

Key Practices

1. Start with the contract (the "stud")

   • Define: purpose, inputs, outputs, side-effects, dependencies
   • Document in README or top-level docstring
   • Keep small enough to hold in one prompt

2. Build in isolation

   • Code, tests, fixtures inside module directory
   • Only expose contract via __all__ or interface file
   • No other module imports internals

3. Regenerate, don't patch

   • When change needed inside brick → rewrite whole brick from spec
   • Contract change → locate consumers, regenerate them too
   • Prefer clean regeneration over scattered line edits

4. Human as architect, AI as builder

   • Human: Write spec, review behavior, make decisions
   • AI: Generate brick, run tests, report results
   • Human rarely reads code unless tests fail

Benefits

• Each module independently regeneratable
• Parallel development (different bricks simultaneously)
• Multiple variants possible (try different approaches)
• AI-native workflow (specify → generate → test)

Implementation Patterns

Vertical Slices

• Implement complete end-to-end paths first
• Start with core user journeys
• Get data flowing through all layers early
• Add features horizontally only after core flows work

Iterative Development

• 80/20 principle: high-value, low-effort first
• One working feature > multiple partial features
• Validate with real usage before enhancing
• Be willing to refactor early work as patterns emerge

Testing Strategy

• Emphasis on integration tests (full flow)
• Manual testability as design goal
• Critical path testing initially
• Unit tests for complex logic and edge cases
• Testing pyramid: 60% unit, 30% integration, 10% end-to-end

What to test:

• ✅ Runtime invariants (catch real bugs)
• ✅ Edge cases (boundary conditions)
• ✅ Integration behavior (full flow)
• ❌ Things obvious from reading code (constant values)
• ❌ Redundant with code inspection

Error Handling

• Handle common errors robustly
• Log detailed information for debugging
• Provide clear error messages to users
• Fail fast and visibly during development
• Never silent fallbacks that hide bugs

Simplicity Guidelines

Start minimal:

• Begin with simplest implementation meeting current needs
• Don't build for hypothetical future requirements
• Add complexity only when requirements demand it

Question everything:

• Does this abstraction justify its existence?
• Can we solve this more directly?
• What's the maintenance cost?

Areas to embrace complexity:

• Security (never compromise fundamentals)
• Data integrity (consistency and reliability)
• Core user experience (smooth primary flows)
• Error visibility (make problems diagnosable)

Areas to aggressively simplify:

• Internal abstractions (minimize layers)
• Generic "future-proof" code (YAGNI)
• Edge case handling (common cases first)
• Framework usage (only what you need)
• State management (keep simple and explicit)

Decision Framework

When faced with implementation decisions, ask:

1. Necessity: "Do we actually need this right now?"
2. Simplicity: "What's the simplest way to solve this?"
3. Directness: "Can we solve this more directly?"
4. Value: "Does the complexity add proportional value?"
5. Maintenance: "How easy will this be to understand later?"

Library vs Custom Code

Evolution pattern (both valid):

• Start simple → Custom code for basic needs
• Growing complexity → Switch to library when requirements expand
• Hitting limits → Back to custom when outgrowing library

Questions to ask:

• How well does this library align with actual needs?
• Are we fighting the library or working with it?
• Is integration clean or requiring workarounds?
• Will future requirements stay within library's capabilities?

Stay flexible: Keep library integration minimal and isolated so you can switch approaches when needs change.

Anti-Patterns (What to Resist)

In Kernel Development

❌ Adding defaults or config resolution inside kernel ❌ File I/O or search paths in kernel (app layer responsibility) ❌ Provider selection, orchestration strategy, tool routing (policies) ❌ Logging to stdout or private files (use unified JSONL only) ❌ Breaking backward compatibility without migration path

In Module Development

❌ Depending on kernel internals (use protocols only) ❌ Inventing ad-hoc event names (use canonical taxonomy) ❌ Private log files (write via context.log only) ❌ Failing to emit events for observable actions ❌ Crashing kernel on module failure (non-interference)

In Design

❌ Over-general modules trying to do everything ❌ Copying patterns without understanding rationale ❌ Optimizing the wrong thing (looks over function) ❌ Over-engineering for hypothetical futures ❌ Clever code over clear code

In Process

❌ "Let's add a flag in kernel for this use case" → Module instead ❌ "We'll break the API now; adoption is small" → Backward compat sacred ❌ "We'll add it to kernel and figure out policy later" → Policy first at edges ❌ "This needs to be in kernel for speed" → Prove with benchmarks first

Governance & Evolution

Kernel Changes

High bar, low velocity:

• Kernel PRs: tiny diff, invariant review, tests, docs, rollback plan
• Releases are small and boring
• Large ideas prove themselves at edges first

Acceptance criteria:

• ✅ Implements mechanism (not policy)
• ✅ Evidence from ≥2 modules needing it
• ✅ Preserves invariants (non-interference, backward compat)
• ✅ Interface small, explicit, text-first
• ✅ Tests and docs included
• ✅ Retires equivalent complexity elsewhere

Module Changes

Fast lanes at the edges:

• Modules iterate rapidly
• Kernel doesn't chase module changes
• Modules adapt to kernel (not vice versa)
• Compete through better policies

Quick Reference

For Kernel Work

Questions before adding to kernel:

1. Is this a mechanism many policies could use?
2. Do ≥2 modules need this?
3. Does it preserve backward compatibility?
4. Is the interface minimal and stable?

If any "no" → Prototype as module first

For Module Work

Module author checklist:

• [ ] Implements protocol only (no kernel internals)
• [ ] Emits canonical events where appropriate
• [ ] Uses context.log (no private logging)
• [ ] Handles own failures (non-interference)
• [ ] Tests include isolation verification

For Design Decisions

Use the Linux kernel lens:

• Scheduling strategy? → Orchestrator module (userspace)
• Provider selection? → App layer policy
• Tool behavior? → Tool module
• Security policy? → Hook module
• Logging destination? → Hook module

Remember: If two teams might want different behavior → Module, not kernel.

Summary

Amplifier succeeds by:

• Keeping kernel tiny, stable, boring
• Pushing innovation to competing modules
• Maintaining strong, text-first contracts
• Enabling observability without opinion
• Trusting emergence over central planning

The center stays still so the edges can move fast.

Build mechanisms in kernel. Build policies in modules. Use Linux kernel as your decision metaphor. Keep it simple, keep it observable, keep it regeneratable.

When in doubt: Could another team want different behavior? If yes → Module. If no → Maybe kernel, but prove with ≥2 implementations first.