Agent engineering
How to Build AI Agents From Scratch
Building from scratch does not mean training a foundation model. It means owning the agent loop and deciding how goals, context, tools, state, permissions, and evaluation fit together.
Direct answer
To build an AI agent from scratch, define a bounded task, represent the agent state, give the model a small set of typed tools, run a control loop that chooses an action or finishes, validate every tool call, persist the trace, evaluate representative cases, and require approval for risky actions.
The minimum agent architecture
A basic agent has instructions, current state, a model, tool definitions, an execution loop, and a stopping condition. Production systems add identity, permissions, durable state, queues, timeouts, logs, evaluations, and cost controls.
- Goal and completion condition
- Structured context and state
- Typed tool contracts
- Validated execution
- Trace and evaluation
Tool design determines reliability
Give tools narrow names, explicit schemas, predictable responses, and clear errors. Validate arguments in application code and enforce authorization outside the model.
Prefer several small tools over one unrestricted function. The model proposes an action; trusted code decides whether and how it executes.
Evaluate the entire loop
Test task completion, tool selection, argument accuracy, source grounding, permission compliance, latency, cost, and recovery from failures. Include ambiguous requests and adversarial input, not only ideal examples.
A practical step-by-step path
- 1
Specify the task
Define inputs, allowed actions, completion, escalation, and prohibited behavior.
- 2
Create typed tools
Implement narrow schemas with authorization and validation.
- 3
Build the loop
Alternate model decisions and tool results until completion or a hard limit.
- 4
Persist and observe
Store state and traces without leaking sensitive data.
- 5
Evaluate before deployment
Run representative cases and gate risky actions.
How to choose your approach
Simple synchronous loop
Short tasks and learning agent fundamentals.
Watch for: Poor fit for long waits or recovery across processes.
Durable workflow
Long-running tasks, retries, and external events.
Watch for: More infrastructure and state design.
Framework-based agent
Teams wanting built-in tracing and orchestration.
Watch for: Framework abstractions can obscure core behavior.
Mistakes that waste the most time
- • Letting the model enforce its own permissions.
- • Using untyped catch-all tools.
- • Running an unbounded loop.
- • Testing only final text instead of tool behavior and state.
Go from agent loop to reliable system
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Explore the CommunityFrequently asked questions
Do I need to train a model to build an agent?
No. Most agents use an existing model and add application-specific instructions, data retrieval, tools, state, and control logic.
What programming language should I use?
Use a language your team can operate and that has strong support for the required APIs, validation, queues, storage, and observability. Python and TypeScript are common choices.
How many tools should an agent have?
Start with the smallest set that can complete the task. Add tools only when the evaluation set proves they are needed and clearly distinguishable.
When is an agent ready for production?
After it passes representative evaluations, has enforced permissions, clear escalation, observability, cost limits, recovery, and an accountable owner.