Agents and AI Coding Systems

Covers code training data, code tokenization, fill-in-the-middle training, code pre-training objectives.

Covers code explanation, bug detection, code review, code search.

Covers completion context, completion ranking, completion latency, completion UX.

Covers docstring-to-code, test-to-code, code generation strategies, generation quality.

Covers sandboxed execution, execution feedback, iterative refinement, execution safety.

Covers functional correctness, pass@k metric, code benchmarks, beyond correctness.

Discover why LLMs require external tools to overcome knowledge cutoffs, computational limits, and hallucinations. Learn about tool-augmented AI systems.

Learn how function calling enables LLMs to invoke external tools and APIs through structured JSON schemas, bridging natural language and executable code.

Learn how the ReAct pattern enables LLM agents to interleave reasoning with tool execution. Master thought-action-observation loops for autonomous AI systems.

Master LLM tool selection through embedding-based routing, hybrid strategies, and semantic interfaces. Learn to build scalable multi-tool agent systems.

Master LLM agent architectures including control loops, state management strategies, planning mechanisms, and termination conditions for autonomous AI systems.

Learn how AI agents manage short-term context and long-term memory. Explore vector databases, retrieval algorithms, and memory hierarchies that enable persistent, learning systems.

Learn to evaluate AI agents with task completion metrics, trajectory analysis, and safety testing. Covers WebArena, SWE-bench, GAIA, and OSWorld benchmarks.

Covers task decomposition, goal-directed planning, plan execution, plan revision and recovery.

Covers agent coordination, communication protocols, role assignment, multi-agent benchmarks.

Covers unsafe action prevention, agent alignment, sandboxing, monitoring and intervention.

Screenshots, accessibility trees, action spaces, grounding, recovery, and evaluation on real interfaces.

Repository navigation, test-driven changes, execution feedback, code review, and long-running tasks.

Search, source selection, evidence synthesis, citation integrity, uncertainty, and reproducible research traces.

Checkpoints, resumability, queues, idempotency, retries, deadlines, and state across long-running workflows.

MCP clients, servers, tools, resources, prompts, capability negotiation, transport, and trust boundaries.

Discovery, task delegation, status, artifacts, interoperability, and failure semantics across agents.

Approval gates, reversibility, escalation, authority, interruption, and clear ownership of consequential actions.

Functional success, partial credit, recovery, cost, time, safety, and contamination-resistant task suites.