MediaClaw Super-Agents
MediaClaws: bounded, observable expert agents with a Cognitive Rig, Evidence Cards, human-controlled skill promotion, and mastery scorecards.
A deterministic container around a probabilistic brain. A MediaClaw is what happens when an LLM-powered agent gets the structure of a real production worker — a structured operating loop, an evidence trail, governed skills, and a learning loop that compounds run over run.
A MediaClaw runs domain-specific media tools and gets better at them each week. Work that used to take a coordinated multi-person handoff completes in seconds, with the operator's judgment retained at the points that matter.
What's inside
Four mechanisms work together:
1. Cognitive Rig
A Cognitive Rig is the structured Plan → Execute → Validate → Learn operating loop that makes a MediaClaw bounded, observable, and repeatable, recording the evidence needed to inspect how a run progressed.
2. Evidence Cards
One immutable record per step. Every step writes a card capturing what ran, what it produced, and the trace ID that joins it back to the gateway and audit chain; usage is captured for billing attribution and settlement. Cards are append-only and immutable — enforced at the platform's data layer, not by convention. When a stakeholder asks "why did the agent do that?", you open the cards.
3. Skills Governance
A reusable skill is a tool-using competency a MediaClaw can deploy. New and revised skills are validated against the current approved skill before they can be used, and require human approval before activation. The authenticated product shows each skill's review state.
Promotion is human-controlled. The Learning Loop can propose a promotion; it cannot execute one.
4. Learning Loop
Every successful run is a candidate for heuristic extraction. The Learning Loop reads the run's evidence trail, looks for patterns worth promoting, and drafts a proposal — a refined skill, a new heuristic, an updated operating directive. Proposals enter governance as pending proposals and are not deployed without human approval.
The Learning Loop does not modify model weights. There is no fine-tuning. The model is whichever foundation model the team has chosen; the Learning Loop only changes the skill library and the cognitive-rig directives that wrap it.
How MediaClaws compose
A MediaClaw can call another MediaClaw. A Podcast Ingest claw can call an Asset QC claw which can call a Brand Safety claw — each one its own governed, billable surface. Composition does not collapse governance: every cross-claw call goes through the gateway, gets a trace ID, produces evidence, and is metered against the calling organization's credits.
Composition is how a single agent task that used to take a half hour across three operators becomes a half-minute across three claw calls.
What you commit to when you ship a MediaClaw
A MediaClaw is not a one-shot script. Shipping one is a commitment to:
A Cognitive Rig definition — the operating loop's shape and the validation criteria.
A skills set — versioned and governed, with promotions reviewed by humans.
An evidence contract — every step writes a card; cards are append-only.
A billing surface — every step is metered through the gateway.
In return, the platform gives the claw governed tool access, durable context, inspectable runs, and learning over time.
What MediaClaws are not
They are not a generic LLM chat. The container enforces structure; every action goes through a defined stage of the operating loop and a governed tool.
They are not unrestricted. Every tool call passes through the gateway, and the cognitive rig only reaches tools the platform has governed.
They are not unmanaged learners. The Learning Loop's outputs are proposals, not actions. Humans approve.
Where to go next
New to the platform
Studying decision memory and the learning loop in depth
Looking at how MediaClaw surfaces are discovered
Looking for the gateway internals that govern claw runs
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