Agentic AI resources

A clear introduction to AI systems that pursue goals, use tools, and adapt across multiple steps.

How fixed automation, model-powered workflows, and autonomous agents differ in control and reliability.

The control cycle behind most tool-using AI agents, from initial goal to a verified stopping point.

What the language model contributes—and what must be handled by the surrounding application.

How agents move beyond text by invoking APIs, databases, browsers, and application functions.

The difference between working context, durable memory, retrieval, and application state.

When agents need explicit plans, how plans change, and why execution feedback matters more than elegant reasoning.

A practical spectrum from suggestions to supervised actions and bounded autonomous execution.

A framework-neutral path from one narrow goal to a controlled agent loop with a real tool.

Design instructions that define purpose, boundaries, tool behavior, and escalation without becoming brittle.

Principles for tool names, parameters, responses, errors, idempotency, and safe execution.

Why typed responses make routing, validation, storage, and user interfaces more dependable.

How agents search private knowledge, decide what to retrieve, and ground answers in evidence.

Where approval, review, correction, and escalation create the most value in agent workflows.

When specialized agents help, how they coordinate, and why more agents do not automatically mean better results.

Combining model judgment with explicit workflow states, transitions, and recovery paths.

Patterns for durable tasks that span minutes, hours, approvals, retries, and changing external state.

Measure task success, tool behavior, evidence quality, safety, cost, and consistency across full trajectories.

Create representative tasks, expected outcomes, and edge cases that reveal meaningful regressions.

What to record across prompts, model decisions, tool calls, state changes, approvals, and outcomes.

A field guide to loops, premature success, bad tool calls, stale state, weak evidence, and silent escalation failures.

Why untrusted content can redirect agents and how layered controls protect tools, data, and users.

Apply least privilege, scoped credentials, isolation, and approval boundaries to agent actions.

Control model calls, context growth, tool delays, retries, and quality tradeoffs in multi-step systems.

Minimize sensitive context, control retention, isolate tenants, and make agent data flows understandable.

A practical sequence for validating scope, quality, security, operations, and rollback before launch.

Find work where adaptive decisions create value and errors remain detectable, recoverable, and bounded.

Decide what to own across models, orchestration, tools, memory, evaluation, observability, and governance.

Make goals, progress, evidence, approvals, uncertainty, and control understandable to users.

Assign ownership and controls based on capabilities, data access, action impact, and evidence of reliability.

How shared conventions can connect models to tools, context, services, and other agents without erasing trust boundaries.

How delegated software may reshape roles, coordination, management, and the value of human judgment.

What changes when software can search, compare, negotiate, and transact on a user's behalf.

The open problems shaping more capable, efficient, secure, and understandable autonomous systems.