Architecting AI Agents: Multi-Step Reasoning and Execution Loops

The Challenge

You built a chatbot that can call tools. Now product asks it to autonomously plan multi-step tasks (book travel, update tickets, triage incident).

Suddenly:

The agent loops forever on ambiguous goals

External APIs get spammed by repeated calls

One bad plan causes high cost and data leaks

Discussion: How do you make an autonomous agent that is useful, safe, and predictable under load?

1. Agent types and when to use them

Agent Type | Behavior | Use Case
Reactive | Single step decisions, no planning | Chat replies, simple tool calls
Deliberative | Plan then execute multiple steps | Complex workflows, multi-call orchestration
Hybrid | Mix planning and reactive fallback | Most production agents

Rule: Start with reactive or guided agents. Add autonomy only when you can observe and control every action.

2. The agent's execution loop (Core pattern)

An agent is an execution loop: observe → plan → act → observe.

sequenceDiagram
participant User
participant Agent
participant Planner
participant Executor
participant Tool

User->>Agent: Goal
Agent->>Planner: Create plan
Planner-->>Agent: Plan steps
Agent->>Executor: Execute step 1
Executor->>Tool: Call external api
Tool-->>Executor: Result
Executor-->>Agent: Step result
Agent->>Planner: Feedback for replanning
Agent-->>User: Final result

Key decisions: step granularity, synchronous vs asynchronous execution, and whether to checkpoint state after each step.

3. Planner vs Executor separation

Keep planning and execution separate.

flowchart TD
A[Input Goal] --> B[Planner]
B --> C[Plan Store]
C --> D[Executor]
D --> E[Tool Calls]
E --> F[Results Store]
F --> G[Replanner]

Why:

Observability: trace planning decisions separately

Safety: validate plans before execution

Retry and resume: checkpoint plans so restarts are bounded

4. Tool interface design (Define contracts)

Treat each external tool like a strict API you control.

Tool contract fields:

name

arguments schema

idempotency key requirement

required auth scope

cost estimate per call

Example tool spec (conceptual):

tool sendEmail
args { to string, subject string, body string }
idempotency required true
auth scope email_send
estimated cost small

Enforce contracts with runtime validation and dry-run mode from the planner.

5. Safety patterns for agents

Dry run / plan approval

Always present plan summary for human approval for high risk tasks.

Idempotency keys

Require idempotency for any effectful tool call.

Rate limits and quotas

Enforce per-agent and per-user quotas to stop runaway costs.

Policy engine

Pre-validate plans against guardrails (data exfiltration, PII, forbidden actions).

Sandboxing tools

Run dangerous tools in limited environments and require extra verification.

6. State, Checkpointing, and Resume

Design state so agents can resume after failure without redoing external effects.

Patterns:

Checkpoint after each step: persist step index, intermediate artifacts, and partial outputs

Two-phase commit for multi-step transactions: prepare → commit (use sparingly and only when all tools support rollback)

Compensating actions: for non-rollbackable steps, register compensator tasks

flowchart TD
A[Plan] --> B[Step 1]
B --> C[Checkpoint 1]
C --> D[Step 2]
D --> E[Checkpoint 2]
E --> F[Complete]

7. Observability and explainability for agents

Trace and expose:

Plan version and rationale for each step

Tool call arguments and responses (masked for PII)

Decision provenance (which prompt and context produced each plan)

Resource usage per step (tokens, time, cost)

flowchart LR
Agent --> Trace
Trace --> Dashboard
Trace --> Alerting

Explainability reduces debugging time and supports audit requirements.

8. Testing agents: Unit, Integration, Chaos

Test tiers:

Unit: planner heuristics and prompt outputs using deterministic model settings

Integration: execution with stubbed tools

Contract tests: tool schemas and idempotency keys

Chaos tests: simulate tool failures, network partitions, and partial responses

Cost tests: estimate tokens and calls for representative workloads

Tip: Use deterministic model settings for repeatable tests (temperature 0 and fixed seeds).

9. Cost and throughput controls

Agents can blow budgets quickly. Control knobs:

Max steps per plan (hard cap)

Cost budget per request (reject or degrade when exceeded)

Dynamic model selection per step (large model only for planning; smaller for templated text)

Caching of tool results and intermediate artifacts

flowchart LR
A[Request] --> B[Budget Check]
B -->|ok| C[Planner]
B -->|exceed| D[Reject]

10. Example: Task automation agent (Booking workflow)

Scenario: Agent must book travel: check flights, reserve, charge card, send confirmation.

flowchart TD
UserGoal[User Goal] --> Planner
Planner --> PlanStore
PlanStore --> Executor
Executor --> CheckFlights
Executor --> ReserveSeat
Executor --> ChargeCard
Executor --> SendConfirmation

Safety enforcement:

Require plan approval before ChargeCard

Use idempotency for ReserveSeat and ChargeCard

Log decisions and attach trace id to payment call

11. Dealing with ambiguity and infinite loops

Agents can loop when goals are underspecified.

Defenses:

Clarification step: require confirmation when plan confidence is below threshold

Step budget: cap maximum steps and abort with explainable reason

Divergence detection: monitor for repeated states and abort when seen X times

12. Metrics that matter for agents

Plan success rate

Average steps per task

Tool failure rate per tool

Cost per successful task

Human approval rate (if used)

Time to completion and first token latency

Capture these per agent version to measure regressions.

Discussion prompts for engineers

Where do you draw the line between autonomous action and human approval?

How do you design idempotency for tools that cannot be rolled back?

What's your preferred checkpoint frequency balancing cost and resume granularity?

How would you simulate a malicious planner that tries to exfiltrate data?

TL;DR — Agent Design Cheat Sheet

Separate planner from executor

Treat tools as strict contracted services with idempotency

Checkpoint after steps and make resume explicit

Enforce safety via dry-run, policy checks, and human approval for risky actions

Instrument every plan and step for observability and cost tracing

Test with deterministic settings, integrate chaos testing, and cap budgets

Takeaway

Autonomy is powerful — and dangerous — when unchecked

Production agents are safe only when they are transparent, auditable, and bounded

Build slow, observe fast, and never deploy a fully autonomous agent without cost controls, human-in-the-loop options, and a thorough testing and monitoring pipeline