How to build an AI agent: the loop, the tools, and the hard parts

What an AI agent actually is, and what it is not

An AI agent is a large language model that uses tools in a loop to accomplish a goal: it plans, takes an action (usually a tool or function call), observes the result, and repeats until the task is done or a stop condition is hit. Anthropic's working definition is exactly this, where an agent is an LLM that dynamically directs its own process and tool usage. The building block underneath is the augmented LLM, a model equipped with retrieval, tools, and memory that generates its own queries and decides what to keep.¹

The single most useful distinction here is agent versus workflow, and most teams get it wrong. In a workflow, the LLM and its tools are orchestrated through predefined code paths that the developer fixes in advance. In an agent, the model decides its own path at runtime. Many systems shipped as "AI agents" are actually workflows, and for well-understood tasks a workflow is usually the more reliable and cheaper choice. Reach for a true agent only when the path cannot be known ahead of time. For the full definitional treatment of what agents are and where they fit, see our pillar on what AI agents are.

An agent also differs from a plain chatbot. A chatbot is largely a stateless request and response: it answers one query, holds no memory across turns, calls no tools, and takes no action in external systems. An agent is multi-step and stateful, and it can act, writing to a database, calling an API, or sending a message, rather than only talking. The contrast below sets the three apart.

The core architecture: the agent loop and its six parts

Every agent runs the same loop: the model plans, decides whether to call a tool or finish, executes the chosen tool, observes the result, appends it to the context, and repeats under a hard iteration cap. Underneath that loop sit six parts: the model, the tools, short-term memory, long-term memory, planning, and the loop controller with its guardrails. Get the loop and the tool interface right and the model is rarely the limiting factor.

The mechanical loop with a tool-using model is concrete. You pass the model a set of tools; it returns a tool-use request with the function name and arguments; your code runs the function and returns the result; you call the model again with that result appended to the message history. The model can request several tools in parallel, and when tool choice is automatic it decides each turn whether to act or answer. Anthropic's guidance is to design this agent-computer interface with the same care as a human interface: clear tool names, precise descriptions, example usage, and edge cases. On real benchmarks they spent more time optimizing the tools than the prompt.²

The six parts break down as follows.

• Model. The reasoning engine that plans and decides. Start with the most capable model to set a quality ceiling, then trade down to cheaper or faster models per task where evals show they hold up.³
• Tools and function calling. Functions you define with a name, a description, and a JSON schema for inputs. Prefer a few well-described tools over many overlapping ones, and use strict schema conformance where available.²
• Short-term memory. The running message history, intermediate tool results, and scratchpad reasoning held in the context window. This is what lets the agent reference earlier steps within a single task.
• Long-term memory. Knowledge that outlives one run, usually through retrieval-augmented generation: documents are chunked, each chunk is turned into an embedding by an embedding model and stored in a vector database, and at query time the most similar chunks are returned as context.⁴ For the mechanics in depth, see our guide to retrieval-augmented generation.
• Planning. How the goal becomes steps. In a true agent the model decides the next step from feedback; in a workflow the steps are fixed. Common patterns include prompt chaining, routing, orchestrator-workers, and evaluator-optimizer, where one call generates and a second critiques.¹
• Loop controller and guardrails. The code that runs the observe-and-act cycle, enforces a maximum iteration count and stop conditions, validates inputs and outputs, and can pause for human review at checkpoints. Anthropic recommends a maximum number of iterations to keep control.¹

How to build an AI agent, step by step

Build an AI agent in eight ordered steps: pick a narrow, measurable use case; choose a capable model; define your tools with clean JSON schemas; add retrieval and memory; write the observe-and-act loop with a hard iteration cap; add outcome-based evals; add guardrails; then deploy to real users with tracing. The biggest timeline driver is scope clarity and data readiness, not raw engineering effort.

Each step has a clear "what good looks like" marker, and the early steps cost the least but decide the most.

1. Pick a narrow, valuable use case. Start where the task is well-scoped, repetitive, and measurable, and where a wrong answer is low-risk, such as drafts, summaries, or internal triage, before automating high-stakes actions.³
2. Choose the model. Begin with the most capable model to set a quality baseline, then trade down per task where evals pass.³
3. Define the tools. Write each tool with a precise name, description, and JSON schema, and treat the interface as a product. Few clear tools beat many overlapping ones.²
4. Add retrieval and memory. Wire in retrieval for domain knowledge plus short-term working memory. For knowledge-heavy agents the bottleneck is usually retrieval quality rather than the model.⁴
5. Write the loop. Implement observe-and-act with a hard maximum-iteration cap and clear stop conditions, or adopt a framework that provides the loop for you.
6. Add evals. Build a small eval set from day one and judge final outcomes and state, not just whether the right API was called. Pair automated evals with human review, which catches what automation misses.⁵
7. Add guardrails. Layer input and output validation: relevance and safety filters, PII screening, and tool-risk gates that route high-impact actions through human approval.³
8. Deploy and monitor. Ship to a small set of real users, then add tracing over the full multi-step trajectory of tool calls, latency, cost, and failures, and iterate.⁶

Observability is now table stakes rather than a nicety. LangChain's 2025 survey found that about 94% of organizations already running agents in production have some form of observability in place.⁶ If you would rather have a team build and run this end to end, see how our AI agent development pods scope, build, and operate agents.

Frameworks and build-vs-buy in 2026

You have three broad paths: hand-code the loop for maximum control, adopt a framework that gives you the loop, tools, and orchestration out of the box, or use a low-code platform for speed at the cost of control. The main framework options in 2026 are the OpenAI Agents SDK, native tool use on Anthropic's Claude, LlamaIndex for data-heavy agents, and graph or role-based frameworks such as LangGraph and CrewAI. Frameworks move fast, so treat the positioning below as a 2026 snapshot.

The table compares the primary options. Vendor SDKs are the most accurate to cite; positioning for the broader ecosystem changes release to release, so verify the current docs before committing.

For production, the boring choice is often the right one. A workflow or a single agent on a thin SDK tends to beat a heavy multi-agent framework on reliability and cost, so start simple and add framework machinery only when a concrete need shows up. The IBM framing of code versus framework versus platform is a useful map: hand-coding gives the most control and the most manual work, frameworks give you scaffolding, and platforms trade control for speed.⁷

Why agents fail in production, and how to harden them

Agents fail in production for five recurring reasons: cascading errors across multi-step runs, genuinely hard evaluation, hallucinated tool calls, cost and latency, and a new class of security risks from autonomy plus tools. None are reasons to avoid agents; they are the work that separates a demo from a system. Hard iteration caps, strict tool schemas, outcome-based evals, least-privilege tools, and human approval on risky actions cover most of it.

Take them in turn. Cascading failure is the structural one: because an agent runs many steps, a single bad tool selection or misread result can compound through the rest of the run, and systems get more brittle as the step count grows. Evaluation is hard because agents can take completely different valid paths to the same goal, so you usually cannot check whether they followed the "correct" steps; judge the final outcome and state, and add human review. Hallucinated tool calls and fabricated outputs are a leading failure mode and can even fool an automated evaluator that reads only the trajectory, so validate tool outputs, use strict schemas with retries, and ground the agent with retrieval.⁵

Cost and latency are a deliberate trade. Anthropic measured that agents use roughly 4x more tokens than a chat interaction, and multi-agent systems about 15x more, which is why a single agent should be the default and multi-agent reserved for genuinely high-value, parallelizable work.⁵ The same research used an orchestrator-worker shape, where a lead agent spawns specialized subagents, but only because the task justified the token cost; for the full single-versus-multi treatment see our guide to multi-agent systems.

Security is the newest hard part. Autonomy plus tools plus memory creates attack classes beyond plain model risk. The OWASP Top 10 for Agentic Applications, released in December 2025, names goal hijacking, tool misuse, identity and privilege abuse, and memory poisoning, and prompt injection remains the dominant driver of agentic failures in production.⁸ Mitigate with least-privilege tool scopes, human approval on high-risk actions, input and output filtering, and sandboxing for any code execution. For the full controls, see our guide to AI security best practices.

Kanika Mathur

Head of Service Delivery, Resourcifi

Kanika Mathur is Head of Service Delivery at Resourcifi, where her engineering pods build tool-using agents and ship the unglamorous parts that decide whether one survives contact with production: tight tool schemas, hard iteration caps, outcome-based evals, and human approval on risky actions. She has watched more agents fail on a bad tool description than on a weak model, which is the bias this guide carries throughout.

Resourcifi on LinkedIn →

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