#reliability
reliability shows up across 5 section(s) and 37 page(s) in this workspace. Use this page as a topic map, not just an archive.
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Where it appears
- Systems 21 page(s)
- Sentences 3 page(s)
- Self 3 page(s)
- Shelf 8 page(s)
- Sticky Notes 2 page(s)
AI Agents vs AI Workflows
A practical explanation of the difference between autonomous-seeming agents and controlled workflows, and why the distinction matters in production systems.
Context Windows as Working Memory
Why context is limited, expensive, and shapes reliability.
Agent Instructions and Handoff as an Operating System
A practical architecture for running AI agents reliably using instruction contracts, handoff memory, and measurable quality gates.
Decision-Making Under Uncertainty in AI Runtimes
A practical framework for making accountable decisions in AI systems when evidence is partial, time is limited, and outcomes are high-impact.
Drift, Decay, and Silent Failure
How systems degrade quietly before they break loudly.
Designing Reusable AI Skills
How to design AI skills with clear boundaries, input and output contracts, tool limits, side-effect controls, and escalation paths.
Engineering Agentic Systems for Reliability
A practical reliability model for agentic systems built around governed steps, verification, escalation, and observability.
Evaluation as a Runtime Discipline
Why evaluation should live inside the operating loop of an AI system instead of being treated as an occasional review ritual.
Evaluation Is a Human Problem
Why benchmarks are not enough and judgment defines quality.
From Ad-Hoc Prompts to Repeatable Agent Workflows
A practical case study showing how structured instructions, handoff memory, and quality gates improved consistency and coverage in this repository.
Knowledge Management as Runtime Memory
Why modern AI teams should treat knowledge management as a live runtime memory system, not a static documentation archive.
Probabilities, Not Truth
Why AI models sound confident even when they are wrong, and why hallucination is a feature of probabilistic systems, not a bug.
Observability First: How AI Systems Learn After Launch
Why observability is the missing layer between model output and reliable product behavior in production AI systems.
Retrieval-Augmented Generation in Plain Terms
How retrieval grounds outputs and where it can still fail.
Skill Evaluation and Versioning
How to define expected behavior, detect regressions, version skill changes safely, and decide when rollback is the right move.
Structured Output and Why It Matters
Why format turns a response into a system you can trust.
What Large Language Models Are Optimized For
Why next-token prediction shapes both capability and failure modes.
Why Most AI Projects Fail After the Demo Stage
Why AI projects often stall after promising demos: weak integration, missing governance, low observability, and unclear adoption design.
Agentic Orchestration: Designing Multi-Agent Coordination
How to design reliable multi-agent systems with proper handoff protocols, coordination patterns, and failure handling that keeps orchestration from becoming orchestration chaos.
Engineering Bounded Autonomy into AI Systems
How to design autonomous AI systems with safety constraints, operational boundaries, and governance hooks that keep autonomy useful without letting it become uncontrolled.
The Logic Void: Where AI Reasoning Breaks Down
Where AI reasoning reaches its boundaries, why those boundaries matter for system design, and how to build reliable systems that acknowledge the limits of logic.
Autonomy needs a brake.
Observability turns behavior into knowledge.
Verification turns output into evidence.
Decision Logs Beat Memory
Why I now log decision rationale instead of trusting recall when AI workflows become ambiguous.
How I Run a Weekly Eval Loop
A small review ritual for checking whether my AI workflows are getting clearer or only getting faster.
The Weekly Observability Reset
A small weekly ritual that keeps my AI workflows honest after launch.
Soothsayer MCP kernel: from prompts to controlled orchestration
How I built a policy-governed MCP runtime where models can reason freely but execution stays deterministic, verifiable, and auditable.
Notes: Observability Logbook Pattern
A compact weekly review format for tracing decisions, evidence, and outcomes in AI workflows.
Architecting Agent Intelligence deck
A practical deck on agent architecture, control points, and reliability patterns.
Engineering Agentic Systems deck
An engineering-focused deck on building agentic systems with explicit control points, checks, and observability.
The I-7 Reliability Standard deck
A companion deck to the I-7 loop with reliability-focused stage-by-stage framing.
Retrieval and grounding evaluation kit
A compact resource pack for checking whether an AI system retrieves the right evidence before it answers.
The I-7 Loop for Reliable AI (video)
A walkthrough video of the I-7 reliability loop with emphasis on checkpoints, governance, and recovery paths.
Engineering Bounded Autonomy deck
A technical guide to engineering AI systems with constrained autonomy, safety guards, and operational boundaries.