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cachly-dev

Cachly — AI Cognitive Brain

madc_deliberate

Resolves conflicting lessons through multi-agent deliberation. Specialist experts vote based on domain knowledge to supersede or flag contested lessons, storing resolution permanently.

Instructions

Multi-Agent Deliberation Chamber (MADC — Layer 3): When conflicting lessons exist for a topic, run deliberation between 6 specialist expert agents (InfraAgent, AuthAgent, DeployAgent, DatabaseAgent, DebugAgent, APIAgent). Each agent votes based on its domain CKG coverage. Unanimous vote → loser superseded. Split vote → contested flag, causal_trace required before acting. Resolution stored as permanent CKG node. Called automatically when learn_from_attempts detects a contradiction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instance_idYesBrain instance ID
topicYesTopic to deliberate, e.g. "fix:jwks-rotation"
contextNoOptional context for the deliberation
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description fully explains the tool's behavior: 6 agents vote, unanimous vs split outcomes, resolution stored as permanent CKG node. It does not disclose side effects like potential data deletion or rate limits, but the core process is transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is detailed but concise, with no redundant sentences. Each sentence adds essential information: condition, process, outcomes, and automatic invocation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, the description covers purpose, inputs, process, and output (resolution storage). No output schema exists, but the description compensates with workflow details. Sibling tools are diverse, and this tool is distinct.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% coverage with descriptions for all 3 parameters. The description adds minimal extra meaning beyond the schema, such as an example for 'topic'. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: to run deliberation between 6 specialist agents when conflicting lessons exist for a topic. This specific verb and resource set it apart from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides when to use it ('when conflicting lessons exist') and notes it is called automatically, giving clear context. It lacks explicit alternatives or when-not-to-use guidance, but the automatic invocation reduces the need for manual selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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