Beyond the Chatbot

Over the last few years, large language models (LLMs) have been most visible as chatbots: you type a question, you get an answer. That interface is useful, but it’s not the end-state.

The bigger shift is moving from text generation to task completion.

When people say “AI agents,” they don’t mean “a smarter chatbot.” They mean systems that can:

• break a goal into steps
• gather information
• call tools (APIs, databases, applications)
• take actions over time
• verify results and recover from errors

In other words, a chatbot talks. An agent does.

This post is aimed at builders: engineers, product leads, and anyone trying to understand what agents are, how they work, where they’re already useful, and what pitfalls to design around.

What Is an AI Agent (Operational Definition)

At a practical level, an AI agent is:

An LLM-driven control loop that can plan, take actions via tools, observe outcomes, and iterate until it reaches a stopping condition.

That’s a deliberately boring definition—and that’s good. It keeps the focus on system behavior, not hype.

Most agents have three core ingredients:

1. A policy: usually an LLM prompt + model + decoding configuration (the “brain”).
2. Tools: functions the agent can call to interact with the world (APIs, search, code execution, databases).
3. State: memory of what’s happening (the goal, intermediate results, constraints, previous actions, and sometimes long-term user context).

The magic is not that an LLM “becomes autonomous.” The magic is that we wrap the LLM in structure.

The Anatomy of an Agent Loop

A minimal agent loop looks like this:

1. Receive goal (e.g., “Summarize these documents and draft an email”).
2. Plan next step (what to do now).
3. Act (call a tool or ask a clarifying question).
4. Observe tool output.
5. Update state (store results, adjust plan).
6. Repeat until:
   • the goal is satisfied
   • a budget is hit (time, tokens, cost)
   • a human approval is required
   • the agent decides it cannot proceed safely

This loop is “agentic” even if it’s narrow and heavily constrained. In fact, most production agents should be constrained.

A More Realistic Pseudocode Example

Here’s a simplified sketch that includes the pieces real systems need: structured tool calls, budgets, and explicit stopping.

class Agent:
    def __init__(self, llm, tools, max_steps=12):
        self.llm = llm
        self.tools = tools
        self.max_steps = max_steps

    def run(self, goal, context=None):
        state = {
            "goal": goal,
            "context": context or "",
            "history": [],
            "artifacts": {},
        }

        for step in range(self.max_steps):
            message = self._build_prompt(state)
            decision = self.llm.generate_structured(message)

            # decision = {"type": "tool", "name": "search", "args": {...}}
            # or          {"type": "final", "answer": "..."}
            # or          {"type": "ask_user", "question": "..."}

            if decision["type"] == "final":
                return decision["answer"]

            if decision["type"] == "ask_user":
                return {"needs_user": True, "question": decision["question"]}

            if decision["type"] == "tool":
                tool = self.tools[decision["name"]]
                result = tool(**decision["args"])

                state["history"].append({
                    "step": step,
                    "decision": decision,
                    "observation": result,
                })
                continue

        return {"error": "max_steps_exceeded", "state": state}

Real agents add more robustness (timeouts, retries, caching, idempotency keys, rate limits), but the structure remains.

Tools: Where Agents Become Useful

Without tools, an LLM can only transform text. With tools, it can interact.

Common tool categories:

• Information retrieval: search, internal knowledge bases, document stores, RAG.
• Computation: calculators, code execution, data analysis.
• Communication: email, Slack/Teams, ticket creation.
• Operations: Kubernetes, cloud APIs, feature flags, database queries.
• Transactions: payments, bookings, inventory changes (high-risk).

Tool Design Is Product Design

If you want an agent to be reliable, the tool layer matters as much as the model.

Good tools are:

• Narrow: do one thing well.
• Typed: clear inputs/outputs, ideally machine-validated.
• Idempotent: safe to retry without causing duplicate side effects.
• Observable: logs and traces for every call.
• Permissioned: the agent only gets what it needs.

Bad tools are:

• “do anything” endpoints
• tools that return unstructured blobs
• tools that mix multiple actions
• tools that hide failure modes

If an agent is a junior employee, tools are its training and workplace setup.

Memory: What People Mean (And What They Usually Want)

“Memory” in agent systems can refer to different things:

1) Working Memory (Short-Term)

This is the state for the current task: the plan, partial results, and tool outputs. It’s often just a structured object plus the recent conversation.

Key technique: summarize and compress. If you dump every tool output into the prompt forever, cost and latency explode.

2) Retrieval Memory (Long-Term Knowledge)

This is not “the agent remembers you like coffee.” It’s more often:

• previous tickets
• company docs
• codebase snippets
• SOPs and runbooks

Retrieval memory is typically implemented with search over indexed content (vector or hybrid). The critical part is not the embedding model—it’s the curation and access control.

3) User Preference Memory (Personalization)

This is the most sensitive category. If you store user preferences, you need:

• explicit user consent
• visibility (what is stored)
• edit/delete capability
• strong access controls

Most agent products don’t need deep personal memory to be useful. They need good task context and reliable tools.

Agents vs. Workflows: The Missing Distinction

A helpful mental model:

• Workflow automation: deterministic steps, fixed logic, predictable state transitions.
• Agents: flexible reasoning inside the loop, with tool calls and adaptation.

The best products often combine both.

Example:

• Use a workflow engine for the high-level process (approval gates, retries, scheduling).
• Use an agent for the uncertain parts (triage, summarization, drafting, routing decisions).

This hybrid approach is how you ship agentic systems without making everything probabilistic.

Real-World Use Cases That Actually Work Today

Agents are most successful when the task:

• has clear success criteria
• can be decomposed into tool calls
• is tolerant of partial automation (human-in-the-loop)

Some realistic examples:

1) Support Triage and Drafting

• read the ticket + relevant docs
• propose a category and priority
• draft a response
• suggest next actions

Humans approve; the agent accelerates.

2) Sales and Customer Research

• collect public info about a company
• summarize ICP fit
• draft outreach tailored to their situation

Success depends on good retrieval and careful sourcing.

3) Developer Productivity

• convert natural language into code changes
• generate tests
• explain unfamiliar code

This works best when tools include repository search, compilation/tests, and constraints.

4) Internal Operations

• “Show me the top error sources from last deploy”
• “Create a dashboard for this service”
• “File an incident report draft from these logs”

These are powerful because they’re inside a controlled environment.

The “App-less” Future (A More Grounded Version)

It’s tempting to claim that apps will disappear and we’ll talk to one universal assistant for everything.

The likely near-term reality is more practical:

• software becomes more composable
• interfaces become more intent-driven
• agents become an orchestration layer over existing systems

Instead of:

• opening five apps and performing five micro-tasks

You’ll:

• express intent (“book a flight that doesn’t conflict with my meetings”)
• review a proposed plan (options, constraints, prices)
• approve the final action

The “approval step” is not a footnote. For high-stakes actions, a human confirmation step is a feature, not a limitation.

Risks and Challenges (Where Most Agent Demos Break)

Agents fail in predictable ways. The good news is that many failures can be engineered around.

1) Infinite Loops and Thrashing

If the agent keeps repeating a failing action (e.g., “search again”), it burns cost and time.

Mitigations:

• hard step budgets
• detecting repeated failures
• requiring a plan change after N failures
• escalating to a human

2) Hallucinated Actions

LLMs can “confidently” call a tool with wrong parameters or invent nonexistent entities.

Mitigations:

• schema-validated tool calls
• allowlists for actions
• confirmation prompts for destructive operations
• postconditions (“verify that the user exists before charging”) implemented in code

3) Prompt Injection and Data Exfiltration

If the agent reads untrusted text (web pages, emails), that text can try to trick it into leaking secrets or calling dangerous tools.

Mitigations:

• treat external content as untrusted
• sandbox tool access
• never place secrets directly in the prompt
• separate “retrieval” context from “instruction” context

4) Authentication and Authorization

The scariest agent is not the one that writes bad prose. It’s the one that has your credentials.

Mitigations:

• principle of least privilege (scoped tokens)
• expiring credentials
• per-action permission prompts
• audit logs for every tool call

5) Cost and Latency

Naive agents can be expensive: multiple model calls + multiple tool calls.

Mitigations:

• caching tool results
• smaller models for simpler steps
• summarizing state
• parallelizing independent tool calls (carefully)

How to Build a Useful Agent (Without Building a Science Project)

If you’re building an agent, start narrower than you think.

1. Choose one job. e.g., “draft a weekly status update from Jira + Slack.”
2. Define success criteria. What does “done” mean?
3. Design 3–5 tools. Keep them narrow and typed.
4. Add guardrails. Budgets, allowlists, and human approval.
5. Evaluate with real data. You need examples, not vibes.
6. Instrument everything. Logs, traces, and feedback loops.

If you can’t explain your agent’s scope in one sentence, it’s too broad.

The Big Shift

Agents are not “AGI in disguise.” They are a new application pattern: LLMs + tools + feedback loops.

We’re moving from “software as a tool” to “software as a collaborator”—but only if we build agent systems with clear boundaries, strong tool design, and a lot of respect for safety.