Research

PersonalClaw was designed from a competitive-research corpus before most of it was built, and this section publishes that corpus rather than summarising it. Fourteen topic files, distilled on 2026-07-13 from 95 individually-read sources — agent frameworks, workflow engines, memory systems, harnesses, judging setups, local-inference stacks — each compressed into two sections: Principles, the findings that turned up independently across several systems, and Mechanisms, implementation-ready designs carrying the actual schemas, state machines, thresholds and formulas.

It is here because PersonalClaw is built agentically — researched, planned and largely written by agents, under one owner who reviews and signs every change — and a project that makes that claim should show its working rather than assert it. This corpus is one of the artifacts of that process, published as it was written, unedited for an audience. The 95 source files behind it were read and distilled by agents; the topics they produced are what the roadmap plans were then argued from.

Three things it is not.

It is not documentation of PersonalClaw. Nothing here describes what the shipped product does — the architecture and reference sections do that, and they are generated from the released source. Some of what follows describes designs PersonalClaw has not built and may never build.

It is not a survey, and it is not neutral. The corpus was read to build one product for one person. It says self-grading is structurally broken rather than that opinions differ on self-evaluation, because that is what the evidence said; hedging it would have cost the reader the finding.

It is not maintained. These files record what the sources said in July 2026. The systems they describe have moved on and the files have not. The 95 per-source notes were retired once the topics were written, so the topics are the record — there is no rawer version to consult.

Cross-references between topics resolve inside this site. Everything else, including the corpus’s own index and the roadmap plans these findings fed into, links back to core at the exact commit this site was built from: docs/research/learnings/README.md.

The topics

• Agent Harness Engineering — The harness as the lever over the model: agent loop anatomy, context compaction ladder, tool-output offloading, the ratchet, feedforward/feedback control taxonomy.
• Automation & Triggers — Always-on background work: scheduler-dispatches-never-executes, the full trigger taxonomy, crash-safe firing, boot sweeps, unattended-vs-interactive policy regimes, pull-over-push.
• Ecosystem & Interop — Distribution and interop: packaging as the product, one canonical source with generated projections, self-describing manifests, two-tool retrieval surfaces, supply-chain trust ladders, protocol adapters (MCP/ACP/A2A/O…
• Knowledge Pipelines — Compile-at-ingest knowledge over query-time RAG: claim schemas with quotes and epistemic stance, deterministic identity and idempotent writes, cost-layered extraction, per-item LLM-call budgets.
• Local Models & Inference — Model operations as the differentiator: capability flags over failure-time discovery, sidecar process isolation, declarative catalogs, download UX engineering, hardware tiering/quant ladders, backend benchmarking with…
• Memory Architectures — Typed/tiered memory: injected-memory authority doctrine, relevance-ranks/strength-prunes, extract→decide formation, decay/heat/promotion math, deterministic write-time graphs, graph-walk recall.
• Multi-Agent Orchestration — Coordination: structured state over dialogue, typed handoff schemas, debate as a compilable macro, orthogonal-lens role panels, two-tier model economics, workers-know-their-judge.
• Planning & Task Decomposition — The task/workflow/agent taxonomy and 4-question checklist, complexity triage before the planner, tiered template matching, spec fast lanes (never waterfall), graduated evidence-gated autonomy.
• Product Surfaces & UX — Agent-product UX: generative UI (fault-tolerant streaming spec parsers, registry-constrained rendering, merge-by-name patching), dashboards, decision-ready approval briefs, evidence/proof sections, refresh-on-view.
• Security & Guardrails — Enforcement over request: chokepoint-gated capabilities, untrusted-content fencing and role-token stripping, capability sets frozen at creation, egress policy tiers, budgets and circuit breakers as security controls.
• Self-Improvement Loops — Learning flywheels: the ratchet doctrine, traces as the universal substrate, statistical acceptance gates (median-of-N, held-out, monotonic ratchets), propose-don’t-write, failure attribution before repair.
• Skills & Prompt Craft — Skill authoring: SKILL.md anatomy and three-tier progressive disclosure, trigger-shaped descriptions, the invocation-axis taxonomy, a skill-prose failure-mode lint rulebook, token-budget economics.
• Verification & Judging — Judging: self-grading is structurally broken, a loop’s floor is its evaluator, act-capable skeptical judges executing ground truth, externalized completion, typed verdict contracts, hidden validation.
• Workflow Engine Design — Graph/DAG engines: node taxonomies, declarative specs compiled to immutable versions, event-sourced state law, joins/retries/failure envelopes, rewind/fork/replay, mid-flight mutation, the agentic escape hatch.