Key facts
| Definition | A model-driven loop that plans, acts with tools and iterates toward a goal |
| Core components | Model, instructions, tools, state and a stopping condition |
| Why it matters | Automates multi-step work that a single prompt cannot complete |
| Plugsky support | Live function calling, streaming and an agents API |
| Model choice | 30+ models; cheap aliases for steps, stronger ones for planning |
| Safety controls | Scoped tools, step limits, timeouts, confirmation for destructive actions |
| Evaluation | Task success, steps taken, tool accuracy and cost per completed task |
| Free tier | Free plan with 2 free AI models, no card required |
TL;DR
- An agent is a loop, not a prompt: reason, act, observe, repeat.
- Tools are what separate an agent from a chatbot.
- Bound every loop with step and time limits.
- Start with one agent and clear tools before adding complexity.
- Evaluate end-to-end task success, not individual responses.
How it works, step by step
- Define the goal precisely, including what 'done' looks like.
- List the tools the agent needs and write strict schemas for each.
- Write instructions that state the plan-act-observe loop and stopping rules.
- Add guardrails: step caps, timeouts, permission scopes and confirmations.
- Log every step with inputs, outputs and latency for debugging.
- Evaluate on a labeled task set and iterate on the weakest component.
Try it yourself
The agent loop
An agent alternates between reasoning and acting. The model receives the goal, current state and available tools, then either calls a tool or produces an answer. Tool results become new observations, and the cycle repeats. What makes it an agent rather than a chatbot is that it changes the world — writing files, querying systems, sending requests — and uses the results to decide what to do next.
Components of a working agent
- Model: the reasoning engine; tool-calling ability is mandatory.
- Instructions: role, constraints, loop policy and stopping conditions.
- Tools: schema-defined actions with validated arguments.
- State: short-term context plus optional persistent memory.
- Guardrails: step limits, timeouts, scoped permissions and approval gates.
Weakness in any one component shows up as an agent that loops, stalls or takes unsafe actions.
Common mistakes
- No stopping condition, so the loop runs until it hits rate limits.
- Granting broad tool permissions because scoping takes effort.
- Testing with happy-path tasks only, then shipping without failure handling.
- Using a weak tool-calling model and blaming the tools.
- Evaluating single responses instead of whether the task completed correctly.
Agents on Plugsky
Agents are ordinary application loops over the OpenAI-compatible chat completions API with function calling, which is live. The agents API provides session orchestration, and all 30+ models are reachable through one endpoint, so you can route planning to a stronger alias and routine steps to cheaper ones. Embeddings support memory and retrieval when the agent needs persistent knowledge. For strict environments, private deployment options keep agent traffic inside your network while the code path stays the same.
Begin with a read-only tool set, then widen permissions as trust grows.
Honest comparison
| Capability | AI agent | Chatbot | Scripted automation |
|---|---|---|---|
| Decision making | Model plans next steps | Answers one turn | Fixed branches |
| Tool use | Chooses and calls tools | None or manual | Hard-coded calls |
| Adapts to results | Yes, observes and iterates | No | Limited to defined cases |
| Failure handling | Retries or escalates | Returns an answer | Crashes or logs |
| Best for | Multi-step open-ended tasks | Q&A and support | Stable repetitive flows |
Frequently asked questions
What is an AI agent?
A system where a language model runs in a loop — reasoning, calling tools, observing results and repeating — until it completes a goal or reaches a limit.
How is an agent different from a chatbot?
A chatbot responds to messages. An agent acts: it calls tools, changes state in external systems and uses the results to decide its next step.
Do I need an agent for every task?
No. Single prompts and fixed scripts are cheaper and more reliable for stable workflows. Use an agent when the sequence of steps cannot be predetermined.
Which models should power an agent?
One with reliable function calling. Route planning to a stronger model and routine steps to a cheaper alias from the 30+ model catalogue.
How do I keep an agent safe?
Scope tool permissions to least privilege, cap steps and time, require confirmation for destructive actions, and log every call.
Can I build agents without an agent framework?
Yes. The core loop is a chat completion with tools and a while condition. Frameworks help with orchestration and memory, not with the fundamentals.