Agentic AI
25 concepts
Always-On Agents
AI systems that run autonomously in the cloud on schedules, API triggers, or webhooks — executing complex workflows without requiring a user's local machine.
Managed Agents
Cloud-hosted AI agent platforms that handle infrastructure, credential management, and sandboxing so developers only define tasks and guardrails—dramatically accelerating agent deployment.
Agent Evaluation
The practice of measuring AI agent performance using deterministic, execution-based testing environments that verify complete tool-call trajectories rather than relying on subjective LLM-as-a-judge grading.
Agentic Engineering
The discipline of building autonomous AI agent systems — covering architecture, orchestration, tool integration, safety, and operations.
Agentic RAG
RAG where an autonomous agent controls the retrieval process — iteratively searching, refining queries, and cross-referencing sources.
Context Compression for AI Agents
Techniques to reduce token counts while preserving meaning — critical for agentic workflows that exhaust even million-token context windows.
Kairos
An always-on background daemon inside Claude Code that autonomously prunes, merges, and resolves contradictions in the AI agent's working memory.
Prompt Chaining
Breaking complex tasks into a sequence of simpler LLM calls where each output feeds the next input — improving quality 20-40% over single-pass processing
AI Agent
An AI system that autonomously plans, reasons, and takes actions to accomplish goals using tools
Information Agents
Continuously running AI systems that proactively monitor, synthesize, and act on information across your digital workspace—transforming search from reactive queries into autonomous intelligence.
Multi-Agent Systems (MAS)
Architectures where multiple specialized AI agents collaborate, divide tasks, and verify each other's work — with routing strategies like the Advisor pattern enabling cost-efficient orchestration.
AI Orchestration
AI orchestration coordinates multiple AI models, tools, and data sources into unified workflows, managing the flow between components in complex AI systems.
Agent Operational Memory
A technique that externalises an AI agent's behavioural rules and learned heuristics into structured files loaded at session start, giving the agent persistent and consistent behaviour across restarts without fine-tuning.
Agentic AI
AI systems that combine language models with reasoning and tool-use to autonomously execute complex, multi-step tasks — now supported by dedicated infrastructure for production deployment.
Binex
A local testing framework that orchestrates AI agents using YAML DAGs, providing deep visibility and CLI debugging for multi-agent workflows.
CODREAM
A post-task reflective protocol for multi-agent AI in which agents collaboratively analyse completed tasks, distil insights into compact heuristics, and route that knowledge asymmetrically to teammates who need it most — permanently improving performance without fine-tuning.
Dynamic Cognitive Scaffolding
A technique that lets AI agents build their own reasoning structures at inference time rather than relying on fixed scaffolds, significantly improving performance on complex tasks.
Function Calling
Function calling lets LLMs request the execution of external tools and APIs, enabling real-world actions and data retrieval beyond text generation.
Galactic
An open-source orchestration tool that isolates parallel AI coding agents into separate Git worktrees to prevent file and port conflicts.
Inference-Time Co-Evolution
A training-free paradigm where a population of AI agents dynamically specialises, learns from failures, and restructures its own collaboration topology during execution — without updating model weights.
Proof-Derived Authorization
A security model for AI agents where every action must be backed by a cryptographic proof of its authorisation chain, making prompt injection and unauthorised actions mathematically impossible rather than merely policy-prohibited.
Test-Time Co-Evolution
A training-free technique that evolves how multi-agent AI systems collaborate at inference time, allowing agents to develop specialized roles and route knowledge to where it is needed most.
Trajectory Refinement
A technique that treats an AI agent's action plan as an optimizable object, iteratively refining it through inspection and textual gradient feedback to close the gap between planning and execution.
Difference Between a Chatbot
A chatbot responds to messages in conversation; an AI agent autonomously plans, uses tools, and takes multi-step actions to achieve goals.
Real-World Agent Reliability Gap
The critical gap between AI agent performance on benchmarks (90%+) versus real enterprise workflows (<50%), revealing that frontier models fail at multi-step, ambiguous, tool-heavy tasks humans routinely delegate.