Building an AI agent is no longer mainly about writing a clever prompt.
A production agent combines a language model with instructions, context, tools, state, decision logic, controls, evaluation, and observability so the system can accomplish a goal across multiple steps.
A useful mental model is:
AI agent vs workflow vs chatbot
| System | Behavior | Best suited for |
|---|---|---|
| Chatbot | Responds to messages | Q&A |
| AI workflow | Executes predefined steps | Repeatable processes |
| AI agent | Chooses steps dynamically | Variable tasks |
| Multi-agent system | Multiple agents coordinate | Complex decomposable work |
1. Start with the task
Do not begin with "we want an agent." Start with:
Define objective, inputs, outputs, tools, allowed actions, forbidden actions, approval thresholds, success criteria, failure behavior, and audit requirements.
2. Define the agent contract
Frameworks evolve. The business contract defines the actual system.
A useful contract includes objective, inputs, outputs, tools, permissions, approval boundaries, success criteria, escalation, and audit requirements.
3. Use the simplest viable architecture
Progress only when evaluation shows a need:
Complexity should be earned.
4. Understand the core architecture
A production agent usually contains:
The model is only one component.
5. Choose models based on the job
Different steps can use different models:
- classification → smaller fast model,
- difficult reasoning → stronger model,
- extraction → structured-output model,
- verification → independent evaluator.
Model choice is a knob.
6. Write instructions as policy
Separate purpose, policies, process, tool rules, escalation, and output format.
Avoid one giant instruction block.
7. Give the agent well-designed tools
Tools turn a model from an information generator into an actor.
Good tools make purpose, inputs, side effects, and failure behavior clear.
8. Separate read and action tools
Reading information and changing the world are different risks.
Use progressively stronger controls:
9. Use standard interfaces where useful
Protocols such as MCP can reduce integration coupling. A protocol solves connectivity; governance still decides what an agent is allowed to do.
10. Engineer context
Give the agent the smallest amount of high-signal information needed for the next decision.
Potential context includes user request, conversation history, retrieved documents, tool results, policies, and workflow state.
11. Distinguish memory from context
Context is what the model sees now. Memory is stored information that may be retrieved later.
Memory types can include working, conversational, user, episodic, semantic, and external workflow state.
12. Keep business state outside the model
Persist authoritative state in databases so tasks are recoverable, auditable, resumable, and robust to model changes.
13. Implement the agent loop
Conceptually:
while task not complete:
build context
reason
if tool needed:
execute tool
update state
elif approval needed:
request human
elif complete:
validate and stopProduction loops add retries, timeouts, budgets, authorization, persistence, tracing, and evaluation hooks.
14. Define stopping conditions
Useful limits include max steps, runtime, token budget, spend, tool failures, and confidence thresholds.
These are operational knobs.
15. Build layered guardrails
Apply controls at input, workflow, output, action, and infrastructure layers.
Do not rely on a prompt that says "be safe."
16. Add human oversight based on consequence
Routine read-only actions can be autonomous. High-value, irreversible, regulated, or low-confidence actions should require stronger validation or human approval.
17. Start with one agent
Multi-agent systems add coordination cost, latency, and new failure modes. Use multiple agents only when specialization, parallelism, isolation, or independent verification creates measurable value.
18. Build observability from day one
Capture requests, model decisions, tool calls, results, retries, tokens, latency, cost, and final outcomes.
You cannot debug an agent from its final answer alone.
19. Evaluate the trajectory
An agent may reach the right answer after unnecessary tools, unsafe access, repeated retries, or excessive cost.
Measure both outcome quality and trajectory quality.
20. Build an evaluation dataset
Include normal, hard, edge, adversarial, tool-failure, and ambiguous cases.
21. Measure multiple metrics
Track task success, tool accuracy, parameter accuracy, policy compliance, groundedness, step efficiency, latency, cost, escalation accuracy, and user/business outcomes.
22. Treat the agent as an experimentable system
Important knobs include model, instructions, tool definitions, context strategy, memory, retrieval depth, max steps, approval threshold, and guardrails.
Compare configurations using evaluation data rather than intuition.
23. Use controlled release
24. Separate deterministic logic from probabilistic reasoning
Use software for arithmetic, authentication, permissions, required fields, and hard limits.
Use models for ambiguity, interpretation, planning, classification, and synthesis.
25. Design for failure
Plan for unavailable tools, stale context, contradictory sources, malformed outputs, loops, and partial execution.
Typical sequence:
26. Use idempotency for actions
Retries should not duplicate real-world effects such as refunds or payments.
27. Secure credentials outside the model
The agent requests controlled capabilities; the tool layer handles identity, authorization, and secrets.
28. Use least privilege
Give each agent only the permissions required for its task.
29. Design for changing models
Separate:
so each can evolve independently.
30. Production reference architecture
A practical architecture is:
with cross-cutting:
31. Practical build sequence
Define the task.
Prototype with one model and a few tools.
Build an evaluation baseline.
Harden authorization, state, retries, and controls.
Add tracing.
Optimize knobs.
Scale autonomy and multi-agent behavior only after measurement.
32. DataKnobs approach
KREATE
Build agents, tools, retrieval, workflows, and data products.
KONTROLS
Constrain permissions, privacy, compliance, approvals, and security.
KNOBS
Expose model, prompt, tool set, retrieval depth, max steps, autonomy, and approval thresholds as measurable variables.
Bottom line
Start with a measurable task, define an explicit contract, use the simplest viable architecture, provide carefully designed tools, engineer context, externalize state, implement guardrails, trace trajectories, evaluate behavior, and expose important choices as knobs.
The strongest agent is not the one with the most autonomy. It is the one that achieves the desired outcome reliably, economically, and within intended controls.