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Concepts and products from the digests — short definitions, deeper guides, and links back to where they showed up.
- Agent harness
- The scaffolding around an AI model — tools, loops, intake gates, policies, and evaluation — that turns a raw model into a usable agent system without changing the model weights themselves.
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- AI Financial Advice: Effective with the Right Prompts2026-08-02
- AI Helps Google Fix More Chrome Bugs in June Than in the Past Two Years2026-07-31
- Curated Claude Code: A New Agent Harness with Intake Gate2026-07-25
- Agent Swarms Evolve: From Experimental Demos to Production Tools2026-07-21
- Training a Harness: Achieving Model and Task Agnostic AI Systems2026-07-20
- Agent loop
- The core cycle an agent runs: observe context, choose an action (often a tool call), apply the result, and repeat until the task is done or a stop condition hits.
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- Agent Swarm
- Agent swarms are collections of AI agents that work together to complete complex tasks by dividing them into smaller, manageable pieces.
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- AI agent
- Software that uses a model to pursue a goal with tools and multi-step actions, rather than answering a single prompt in isolation.
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- AI worm
- A self-replicating malicious instruction that can spread through AI-assisted workflows, altering or manipulating documents.
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- AI-generated prototypes
- Throwaway app sketches produced from prompts. Useful for exploring UX and flows, but usually missing the testing, auth, data, and ops work production systems need.
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- Coding agent
- An AI agent specialized for software work — reading repos, editing files, running commands, and opening PRs — usually inside a terminal, IDE, or CI loop rather than a chat box alone.
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- Cognitive offloading
- The reliance on external tools or technologies to process information or perform tasks that could be handled internally by the brain.
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- Cross-Domain Prompt Injection (XPIA)
- Prompt injection that arrives from outside the chat — documents, email, or other apps — and steers an assistant once that content enters its context.
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- Declarative Workflow
- A method of defining workflows in a readable format like YAML, separating orchestration logic from application code.
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- DeepMind
- Alphabet’s AI research lab, known for frontier models and scientific work; increasingly associated with proprietary product development as well as published research.
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- EU AI Act
- The EU AI Act is a regulatory framework that governs the development and deployment of AI systems within the European Union, categorizing applications into risk levels.
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- Evals
- Systematic tests of model or agent behavior — benchmarks, graded tasks, session scoring — used to measure quality, catch regressions, and compare harness or model changes.
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- Exfiltration
- Unauthorized movement of data out of a trusted boundary — in agent systems, often via tool calls, browsers, or generated content that leaks secrets.
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- FP8-quantized weights
- A technique for reducing the precision of model weights to FP8, decreasing computational load and memory usage.
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- Frontier model
- A top-tier, usually proprietary model at the current capability frontier — the reference point open-weight and smaller models are measured against.
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- Graph orchestration pattern
- A multi-agent coordination style that runs tasks as a dependency graph — ordered stages and handoffs — rather than a free-for-all parallel swarm.
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- GRPO fine-tuning
- Group Relative Policy Optimization: a reinforcement-learning fine-tuning method that ranks groups of model outputs against each other to improve task performance without a separate reward model.
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- Harness Training
- A method of optimizing the environment around AI models to improve their performance without altering the core model.
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- Human-in-the-loop
- A design where a person must approve, edit, or veto agent actions — especially irreversible ones — instead of letting the agent run fully autonomously.
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- Inference cost
- The price of running a model in production — usually measured per token or per request — shaped by model size, hardware, batching, caching, and API margins.
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- Intake gate
- A control point at the start of an agent workflow that checks, shapes, or rejects incoming requests before the agent loop spends tools, tokens, or side effects.
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- Mixture-of-Experts model
- A type of neural network architecture that uses different subsets of parameters for different inputs, optimizing resource use.
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- Model Context Protocol (MCP)
- An open standard for connecting AI systems to tools and data sources through a shared protocol, so each integration does not need a custom connector.
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- Armature Launches Product Analytics for AI Agent Sessions2026-08-03
- Harnessing Multi-Agent Systems with Strands and Bedrock for Automated Prospecting2026-07-19
- Multi-Agent Systems Automate Prospect Discovery with Strands and Bedrock2026-07-18
- Multi-Agent Systems with Strands and Bedrock Automate Lead Analysis2026-07-17
- Multi-agent system
- A setup where multiple specialized AI agents divide work and coordinate — in sequence or in parallel — to complete a larger task.
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- Open-weight models
- AI models whose trained weights are publicly released so others can run, inspect, fine-tune, or redistribute them under the model’s license.
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- OpenAI
- The AI research and product company behind ChatGPT and the GPT model family, spanning open research history and proprietary commercial systems.
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- OpenAI-compatible API
- An HTTP API that mirrors OpenAI’s chat/completions shapes so existing SDKs and agents can swap providers without rewriting client code.
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- Planner Agents
- Planner agents are specialized AI models tasked with breaking down overarching goals into smaller tasks and delegating them to worker agents.
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- Predictive policing
- Using algorithms to forecast where and when crimes are likely to occur, based on historical data.
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- Prefix cache
- A technique that reuses the already-computed attention state (KV cache) for a shared prompt prefix so later turns or agents avoid recomputing the same tokens from scratch.
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- Prompt injection
- An attack that sneaks instructions into content the model will read (docs, email, web pages) so those instructions override the developer’s system intent.
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- recursive self-improvement
- A feedback loop in AI where a system uses its capabilities to enhance its own performance and design better successors.
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- Sandbox escape
- A security vulnerability that allows malicious code to break out of a restricted environment and perform unauthorized actions.
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- Session replay
- A reconstructable record of an agent or user session — prompts, tool calls, and outcomes — so developers can debug failures and grade task success after the fact.
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- Swarm orchestration pattern
- A multi-agent coordination style where many agents work in parallel on overlapping work, trading strict sequencing for throughput and scale.
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- Tool use
- Letting a model call external functions — search, code execution, APIs, browsers — mid-generation so it can act on the world instead of only emitting text.
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- Zero-Day Vulnerability
- A software vulnerability unknown to those who should be interested in its mitigation, such as the vendor.
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