agents

ReAct is the pattern behind most tool-using agents. The signal is why interleaving reasoning with actions (and observing results) beats reasoning alone or acting alone. Here is the answer that connects the prompt pattern to real agent behavior.

Tool calling powers every agent. The signal is knowing the model executes nothing: it emits a structured request your code runs, then you feed the result back. Here is the loop and the details that decide reliability.

Agents are over-applied. The strong answer resists the hype: most tasks want a single structured call, and agents earn their cost only under specific conditions. Here is when to reach for one and how to stop it from compounding errors.

Multi-agent is the most over-applied pattern in AI right now. The signal is resisting it unless the task truly needs specialization or parallelism, knowing the supervisor and handoff patterns, and understanding why coordination amplifies failure.

MCP standardized how agents plug into tools and data, but tool design is what makes or breaks reliability. The signal is knowing what MCP actually standardizes and the principles behind a tool the model can call correctly from its description alone.

Reflection is the pattern where an agent critiques and revises its own work. The signal is knowing when it genuinely helps (external feedback grounds the critique) versus when self-critique alone is theater.

Plan-and-Execute and ReAct are the two dominant agent control patterns. The signal is knowing the upfront-plan vs step-by-step tradeoff, why one is cheaper, and why most production agents end up blending them.

Context engineering is the discipline that replaced 'prompt engineering' for serious agent work. The signal is understanding that what you put in the context window (and what you leave out) drives behavior more than clever wording. Here is the answer.

Code-execution agents are powerful and dangerous: arbitrary model-generated code runs on your infrastructure. The signal is real isolation (containers/VMs), resource limits, and network/filesystem restrictions, not 'trust the model.' Here is the answer.

Tool selection and parameter extraction are where agents quietly fail. The strongest answer treats the tool spec as the only thing the model sees, then fixes the design, not the model. Here is how.

The same model performs wildly differently depending on the harness around it. The signal is knowing that the scaffolding often decides agent quality more than the base model. Here is the answer.

A computer-use agent drives a real browser or desktop through screenshots and clicks. The signal is the perceive-decide-act loop plus the containment for an agent that can click, buy, or delete anything. Here is the answer.

An agent that takes actions is far riskier than one that just talks. The signal is action-level guardrails: least privilege, argument validation, human approval for irreversible actions, and a blast-radius mindset. Here is the answer.

Standard RAG retrieves once and generates; agentic RAG turns retrieval into an iterative, reasoning-driven loop. The signal is the agent deciding whether, what, and when to retrieve, then re-retrieving until the evidence holds.

An agent that retries the same failed action 40 times is a runaway bill and a stuck user. Termination is something you engineer into the harness, not something the model reliably decides. Here is the control layer.

Real tools time out, rate-limit, and return malformed JSON. An agent that assumes every call succeeds is a demo, not a product. Here is the error-handling layer that keeps it alive in production.

Real users type 'what about the new policy?' with no context. Retrieving on those four words returns noise. Here is how strong systems disambiguate before they ever hit the retriever.

When a chain of LLM calls and tools produces a wrong final answer, 'the model was bad' is not a diagnosis. You need to see every step. Here is the tracing layer that turns a black box into something debuggable.

Frameworks promise speed and deliver abstraction you'll eventually fight. Rolling your own is more code but full control. Here is the honest tradeoff, and why many production teams end up thinner than they started.

The industry conflates 'agent' with anything that calls an LLM. The useful distinction is how much control you hand to the model, and more autonomy is not better. Here is the spectrum and the decision rule.

A live-coding round that separates people who have shipped agents from people who have read about them. The signal is a real plan-act-observe loop, tools that hit an actual SQL database, guardrails that stop the obvious failures, and a concrete evaluation plan, not a single prompt that pretends to be an agent.

Linear agents commit to one path and can't backtrack. Tree-of-Thoughts and LATS add search over reasoning paths. The signal is knowing what they buy, what they cost, and when a cheaper loop wins.

A context window is not memory. Real agents need a storage architecture: working buffer, episodic recall, and consolidated facts. The signal is the read/write paths and how you keep memory from rotting.

Tools fail: timeouts, rate limits, bad arguments, garbage output. A naive agent retries blindly or gives up. The signal is classifying errors and matching each to the right recovery, with hard caps.

Multi-agent systems coordinate through handoffs and shared state. The signal is knowing the orchestration topologies, what a clean handoff protocol carries, and why naive multi-agent often loses to one good agent.

Most people know MCP exposes tools. The deeper signal is the full primitive set (resources, prompts, sampling, roots) and the patterns: server composition, sampling for nested LLM calls, and scoping access safely.

Final-answer accuracy hides how an agent got there. The signal is trajectory-level metrics: tool-selection and argument accuracy, step efficiency, and matching against reference paths, plus when exact-match is the wrong yardstick.

Per-step accuracy looks fine, yet a 50-step task fails. The signal is understanding compounding error and the techniques (decomposition, verification, checkpointing) that keep long-horizon agents from collapsing.

Agents can critique and retry their own work. The signal is knowing how Reflexion's verbal feedback loop works, when self-critique genuinely helps versus when it is theater, and what makes it real.

Autonomous agents still need humans at the right moments. The signal is designing checkpoints: where to interrupt, what to surface, how to persist and resume state, without turning the agent into a click-through rubber stamp.

Any content an agent reads can carry instructions that hijack it. The signal is knowing why filtering can't fully solve injection and which containment controls actually bound the damage when it succeeds.

Agents re-send growing context every step, so cost scales worse than linearly. The signal is knowing where the tokens go and the levers (caching, model routing, context trimming, step caps) that cut the bill.

Agents that drive a browser or screen fail in ways chat agents never do: stale DOM, dynamic pages, wrong clicks. The signal is the grounding and reliability techniques that turn a flaky demo into something usable.

Two ways to give an agent an action space: emit a JSON tool call per turn, or emit a program that calls the tools itself. One collapses N round-trips into one. The other is the one you can actually audit. Here is how to choose.

The modern agentic system-design question. The signal is the plan, search, read, synthesize, verify loop with per-claim citations, plus bounding cost and latency. The hard parts are not the writing.

Customer support is the killer applied-AI use case, testing agents, RAG, tools, and the all-important human escalation. The signal is knowing when to resolve, when to act, and when to hand off, safely.

AI code review lives or dies on precision: a few noisy comments and the team mutes the bot forever. The signal is grounding in the diff plus repo context, ruthless false-positive control, and earning developer trust one accepted comment at a time.

Code migration is a high-value agentic task with an unforgiving correctness bar: the migrated code must still work. The signal is grounding in the real repo, transforming in verifiable chunks, and gating on tests, never one giant LLM rewrite.

The trap is replacing the classifier with an LLM. The real-time, imbalance, and adversarial constraints do not go away. The signal is a hybrid: a fast calibrated model scores inline, LLMs investigate the gray zone off the hot path.

A Devin-style agent that turns an issue into a merged PR is the hardest agent to make reliable, because every step can fail and errors compound. Here is the architecture and the loop that keeps it honest.

An agent that clicks around a real website is slow, brittle, and one wrong click from a costly mistake. The architecture is about perception, action, and guardrails around irreversible steps. Here is how to build it.

'What was revenue by region last quarter?' sounds like text-to-SQL, but production analytics agents fail on ambiguity, wrong joins, and confidently wrong numbers. Here is the architecture that makes the answers trustworthy.

Direct injection is the user attacking the prompt. Indirect injection hides the attack inside a page, doc, or email the model reads. The signal is realizing retrieved and tool data is untrusted and can hijack the model. Here is the answer.

When an agent holds private data, reads untrusted text, and can communicate out, injected instructions can steal data. This 'lethal trifecta' is the defining agent vulnerability. The fix is architectural, not a better prompt.