Context Studios
AI Knowledge Base 2026

AI Glossary 2026

Clear definitions for the era of Agentic AI and Spatial Intelligence.

Agentic Infrastructure

Sandbox Agents

Sandbox Agents are AI agents that run inside an isolated execution environment. Instead of operating directly against production systems, internal networks, or live databases, they work within a controlled sandbox with explicit limits for filesystem access, network egress, permissions, and runtime duration. In practice, teams implement this through containerized runtimes, short-lived workspaces, policy-based tool permissions, and full audit logging. The key benefit is containment: if an agent makes a bad decision, hallucinates, or triggers an unexpected action, impact stays inside the sandbox rather than propagating into core systems. For agentic workflows that execute code, call APIs, or manipulate files, Sandbox Agents become a core safety and governance layer. They do not replace solid prompt and tool design, but they provide the technical guardrails needed for reliable production deployment. Mature implementations usually pair Sandbox Agents with approval gates, monitoring, and rollback paths so teams can ship faster without compromising security or compliance.

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Inference & Engineering

Schema-First Design

Schema-First Design is a development approach where teams define the interface contract before writing implementation code. Instead of “code first, docs later,” they specify expected fields, data types, required parameters, and error formats up front. Common formats include OpenAPI, JSON Schema, and tool schemas used in the Model Context Protocol (MCP). In AI and agent workflows, this matters because agents can only call tools reliably when inputs and outputs are explicit. A strong schema reduces ambiguity, prevents parsing failures, and makes tool-calling behavior more deterministic. It also improves testing, versioning, and governance, since contract changes become visible immediately. Schema-First Design is therefore more than documentation discipline; it is an operating model for production-grade AI systems. It aligns product, engineering, and operations around one shared contract and turns fragile prototypes into repeatable, scalable integrations.

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Agentic Infrastructure

Self-Hosted LLM

A self-hosted LLM is a large language model that runs in infrastructure controlled by the organization rather than being used only through a third-party API. That infrastructure may be a private cloud, dedicated GPU cluster, on-premises data center, sovereign environment, or isolated customer deployment. The term describes an operating model, not a specific model family. What matters is control over data flows, runtime configuration, model versions, network access, logging, cost behavior, and governance. Self-hosting becomes relevant when teams handle sensitive data, face strict compliance requirements, need predictable latency, or want deeper integration with internal systems. It is not automatically cheaper or better: the organization must still solve deployment, monitoring, scaling, security boundaries, evaluation, fallback handling, and model routing. In practice, the strongest architectures are often hybrid. Routine or sensitive workloads can run in a controlled environment, while managed frontier models are reserved for tasks that need the highest reasoning quality.

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Trust & Sovereignty

SQL Injection

SQL injection is a code injection attack technique in which an attacker inserts or manipulates malicious SQL code into input fields or query parameters of an application, causing the application's database to execute unintended commands. SQL injection remains one of the most prevalent and dangerous web application vulnerabilities, consistently appearing in the OWASP Top 10 security risks. A successful SQL injection attack can enable unauthorized data retrieval, authentication bypass, data modification or deletion, and in severe cases, complete database server compromise. The attack exploits applications that construct SQL queries by concatenating user-supplied input without proper sanitization or parameterized queries. For example, inserting ' OR '1'='1 into a login field may bypass password checks if the query is built via string concatenation. SQL injection vulnerabilities affect applications built on MySQL, PostgreSQL, Microsoft SQL Server, SQLite, and Oracle, regardless of the programming language used. Defense against SQL injection centers on prepared statements with parameterized queries, input validation, stored procedures, principle of least privilege for database accounts, and web application firewalls (WAF). Modern AI-powered code review tools, including those built on Anthropic's Claude and OpenAI's GPT-4, can automatically detect SQL injection patterns during code review, offering a substantial improvement over traditional static analysis tools. At Context Studios, we apply AI-assisted security scanning — including Claude Code security analysis — to identify and remediate SQL injection vulnerabilities in client application codebases as part of our AI security review service.

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Inference & Engineering

SWE-bench

SWE-bench is a standardized benchmark for evaluating how well AI systems can solve real-world software engineering tasks. The benchmark consists of over 2,000 actual GitHub issues from popular open-source projects like Django, Flask, and scikit-learn. Each task includes a problem description, the relevant source code, and automated tests to verify the solution. AI models must analyze the code, identify the root cause of the issue, and generate a working patch — just like a human developer would. SWE-bench has become the primary benchmark for AI coding agents. Current top scores exceed 80 percent (Claude Opus 4.6 achieves 80.8%), demonstrating that AI agents are increasingly capable of solving complex software problems autonomously. Variants like SWE-bench Verified use human-validated subsets for even more reliable results.

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Inference & Engineering

System Prompt

A system prompt is a hidden instruction passed to a large language model (LLM) before any user interaction begins. Unlike regular user messages, the system prompt is typically invisible to end users and defines the behavioral framework, persona, constraints, and context within which the model operates. In practice, a system prompt includes role definitions ("You are a customer support assistant for..."), behavioral rules ("Always respond in English", "Never discuss topic X"), contextual information such as product catalogs or knowledge bases, and formatting guidelines covering response length, tone, and structure. The quality and precision of a system prompt largely determines how reliably and consistently an AI model performs in production. A well-crafted system prompt reduces hallucinations, prevents conversational drift, and keeps the model operating within defined boundaries. Techniques like few-shot examples and explicit output formatting are frequently embedded in system prompts to structure model outputs reliably. In agentic systems, the system prompt takes on an even more central role: it specifies which tools an agent may call, how it handles errors, and what high-level goals it pursues — effectively serving as the operating instructions for an autonomous AI system.

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Reasoning & Reliability

Seedance 2.0

Seedance 2.0 is a multimodal AI video generation model developed by ByteDance, the Beijing-based technology company best known for TikTok. Released in 2025, Seedance 2.0 generates high-fidelity, temporally coherent video clips from text prompts, image inputs, or a combination of both, placing it in direct competition with OpenAI's Sora, Google's Veo 3, and Runway ML's Gen-3. Seedance 2.0 is trained on a large proprietary dataset of video-text pairs and employs a diffusion-based architecture optimized for motion realism, scene consistency, and photorealistic rendering. Key capabilities include multi-shot video generation, camera motion control, character consistency across frames, and support for cinematic aspect ratios. ByteDance designed Seedance 2.0 to power creative workflows inside its own product ecosystem — including CapCut, its popular video editing application — while also making the model available to enterprise API customers. Unlike Sora, which remains accessible only through ChatGPT Plus, Seedance 2.0 offers direct API access, making it a practical choice for developers building automated video production pipelines. The model supports both text-to-video and image-to-video generation, with output lengths ranging from five to thirty seconds. Seedance 2.0 marks ByteDance's most significant entry into the generative video space and signals that AI-native video creation is becoming a core battleground for global tech platforms. At Context Studios, we have tested Seedance 2.0 for automated social media video production and short-form content workflows, evaluating its motion quality against Veo 3 and Sora.

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Agentic Business

Session Continuity

Session continuity refers to the ability of an AI agent or system to maintain state, context, and progress across interruptions, restarts, or session changes. Since LLMs are inherently stateless (no embedded long-term memory), continuity must be explicitly implemented through external mechanisms. The fundamental challenge: each new LLM conversation begins without knowledge of previous interactions. For long-running agent tasks — such as a multi-day research project or a continuously running content process — this is problematic. The solution lies in external state stores and structured context handoffs. Implementation strategies for session continuity: (1) Memory files (state is stored in text files on disk, loaded when resuming), (2) Vector databases (embeddings of prior interactions for semantic retrieval), (3) Structured state objects (JSON documents representing the complete agent state), (4) Event logs (chronological records of all actions enabling replay and resumption). Session continuity architecture typically involves multiple layers: a hot cache for recent context (fast, limited capacity), a semantic memory store for long-term knowledge (slower, unlimited), and an event log for complete reproducibility. The balance between these layers depends on the frequency of context access and the importance of historical fidelity. At Context Studios, session continuity is implemented through daily rotating memory files, a Cortex-based long-term memory system, and structured session logs — a production-grade example of this architecture.

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Agentic Business

Spec-Driven Scaffolding

Spec-driven scaffolding is the practice of controlling AI agents not through free-form prompts but through structured, machine-readable specifications — similar to how software engineers write code against technical requirement documents. Instead of telling an agent 'write a blog post about AI,' a specification precisely defines: format, target audience, minimum word count, required sections, citation obligations, forbidden phrasings, and acceptance criteria. The 'scaffolding' refers to the structural framework of instructions that provides the agent with guidance and prevents drift. Like construction scaffolding supporting a building, the spec scaffold gives the agent a fixed structure to work within at runtime. This structure typically includes: agent role and context, input validation rules, step-by-step deliverables, output format requirements, and explicit boundaries (what the agent should not do). The distinction from classic prompt engineering is fundamental: prompt engineering optimizes for language quality; spec-driven scaffolding optimizes for behavioral consistency. A well-specified agent produces the same structural output on the 1,000th run as on the first — regardless of minor input variations. Spec-driven scaffolding enables a key operational advantage: specifications can be versioned, peer-reviewed, tested, and iteratively improved independently of the underlying model. When a model is upgraded, the specification remains stable — decoupling specification from implementation.

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