AI Agent Reliability

State Management

• Track task progress to avoid duplicate actions and repeated onboarding steps
• Store task state externally (DB, queue) so agents can resume after crashes
• Use a task ID per unit of work to correlate logs, retries, and results
• Idempotent state transitions: applying the same step twice must produce the same state

Idempotency

• External actions should have unique task IDs and safe retry behavior
• Assign a stable idempotency key before any external write or API call
• Check-then-act pattern: verify current state before performing a side effect
• Idempotency is especially critical for emails, payments, and database writes

Traceability

• Record planner thoughts at a safe summary level, tool calls, outputs, and final decisions
• Structured logs > unstructured text: include task_id, step, tool, args, result, latency
• Trace IDs link planner, executor, and verifier spans for end-to-end visibility
• Redact sensitive fields (PII, credentials) before logging tool inputs/outputs

Evals

• Test task success, tool accuracy, hallucination rate, and safety violations
• Evaluate trajectory quality (each step) and final outcome separately — both matter
• Adversarial evals: include edge cases, ambiguous inputs, and hostile tool responses
• Regression suite: run on every model/prompt change before deploying

Fallbacks & Recovery

• Use deterministic workflows when agent confidence is low
• Retry transient failures with exponential backoff; rollback bad actions
• Escalate ambiguous cases to humans rather than guessing
• Abort and alert after N consecutive failures; never loop indefinitely

Tool Registry

• Defines available tools, schemas, permissions, cost, and risk level
• Centralizes tool metadata so agents can discover, filter, and route calls safely
• Tag tools by risk tier (read-only, write, destructive) to enforce policy
• Versioned registry: changing a tool schema without bumping its version breaks agents silently

Function Calling

• Structured tool invocation with typed arguments and deterministic validation
• Validate all arguments against the schema before executing — never trust model output
• Return structured responses; unstructured text wastes tokens and causes parse errors
• Include an error field in every response schema so the agent knows how to recover

Sandboxing

• Run code/file/network actions in isolated environments (Docker, microVMs)
• Limit tool permissions to least privilege: read-only unless write is explicitly needed
• Dry-run mode: simulate irreversible actions before committing
• Network egress controls prevent exfiltration of data via unexpected outbound calls

Permission Model

• User-level, role-level, tenant-level, and action-level access control
• Agents should inherit, not exceed, the permissions of the user they act on behalf of
• Scope tokens and API keys to the minimum actions needed for the task
• Audit permission grants and revocations; alert on unexpected escalation

Approval Gates & Audit Trail

• Ask before sending emails, deleting data, charging money, or changing production state
• Log every tool call, input, output, decision, and failure to an immutable audit log
• Time-box approval windows: auto-reject if the human doesn't respond within N minutes
• Approval gates are the last line of defense before irreversible side effects

Agent Loop

• user goal → planner → tool selection → execution → observation → next step → final answer
• Each iteration produces an observation that updates the agent's working state
• Maximum step count and timeout are mandatory — unbounded loops will eventually hang
• Checkpoint state after each step to enable resume, replay, and debugging

Planner

• Breaks vague goals into concrete steps; should not directly perform risky actions
• Outputs a structured plan (task list, DAG, or step sequence) for the executor
• Dynamic replanning: the planner should revise the plan if an executor step fails
• Planner prompts should be optimized for decomposition, not execution

Executor

• Calls tools/APIs/functions under system control
• Operates on a single step at a time; reports result (success/failure/observation) back to planner
• Should not make planning decisions — escalate ambiguity back to the planner
• Retry logic, timeout handling, and error formatting live in the executor layer

Verifier

• Checks outputs, catches errors, validates constraints before final response
• Can be a separate LLM call, a rule-based check, or both
• Verifier scope: factual correctness, safety policy compliance, format validity
• Failed verification should trigger replanning, not silent delivery of bad output

Memory & Human Approval

• Memory/context stores useful state, but should be scoped and permission-aware
• Human approval is required for destructive, financial, external, or irreversible actions
• Memory should not store secrets or PII unless explicitly encrypted and access-controlled
• Approval history should be persisted so agents don't re-ask for already-granted permissions