AI Agents¶
A chatbot generates responses. An agent can decide a sequence of actions, use tools, and observe what happened before deciding what to do next.
That does not turn the model into an autonomous entity or remove the need to engineer the surrounding system. Quite the opposite: once a model can read email, query a database, execute code or change a record, the problem is no longer only text quality. State, permissions, evaluation, cost and stopping behavior become part of the system.
This series builds a technical and practical map for separating agent hype from the mechanisms that actually make an agent work.
Contents¶
1. What an agent is—and is not¶
- Distinguish a chatbot, workflow, copilot and agent.
- Move from a model that answers to a system that pursues an objective.
- Understand autonomy as delegating concrete decisions rather than handing over complete control.
2. The anatomy of an agent¶
- The observe–plan–act–verify loop.
- Tools, memory, context, state and runtime.
- Why a tool call is a software contract rather than a magical LLM capability.
3. How to evaluate an agent¶
- Reproducible tasks instead of answer-only benchmarks.
- Traces, task success, cost, latency and recovery from failures.
- The problem of an agent being able to "cheat" its own evaluation.
4. Security: when reading data becomes acting¶
- Direct and indirect prompt injection.
- Least privilege, identity, authorization and human confirmation.
- Why a single defensive instruction in the prompt is not enough.
5. From demo to an operable system¶
- Budgets, limits, retries, idempotency and observability.
- Asynchronous work and proactive completion without lying to the user.
- When a deterministic workflow is the better architecture and when an agent adds real value.
The thesis of this series¶
A reliable agent is not the one that acts most often without asking. It is the one that knows what it is allowed to do, can demonstrate what it did, and knows when to stop.
Related series: AI and Generative AI Foundations · Reasoning Models · Multimodality in Generative AI