Agentic AI Best Practices (2026)

AgentMD aligns with IBM's 2026 guidance for agentic AI: observable, adaptive, and accountable systems.

Agent Lifecycle: Where AgentMD Fits

IBM describes AgentOps across five phases. AgentMD maps to each:

Phase	AgentMD Support
Development	AGENTS.md defines objectives and constraints. CLI `init`, `doctor`, `improve` help author and refine the spec.
Testing	`agentmd run . --dry-run` previews execution. Sandbox mode runs in isolation. Contract validation ensures output quality.
Deploy	Human-in-the-loop for deploy steps. Permission boundaries block unauthorized commands. Kill switch cancels running executions.
Monitoring	Execution history, success rates, command-level pass/fail. OTEL export for Langfuse, Datadog, etc.
Feedback	Use failure data to refine `permissions.shell` and guardrails. ROI metrics quantify value.

Concrete Scenarios

Scenario 1: Agent skips a test step

Problem: An AI coding assistant reads AGENTS.md and runs pnpm build and pnpm lint, but skips pnpm test to save time. A broken test slips into the PR.

Solution: AgentMD executes the full spec. Every run includes build, test, and lint in a defined order. No step is optional. Execution history shows exactly what ran and whether it passed.

Scenario 2: Deploy without approval

Problem: An agent is instructed to deploy after tests pass. It does so autonomously—no human review. A misconfiguration reaches production.

Solution: Use policy rules with approval: always for deploy commands. AgentMD blocks execution until a human approves (e.g., via Slack). Audit logs record who approved and when.

Scenario 3: Dangerous command in a prompt

Problem: A user (or compromised prompt) asks the agent to run rm -rf / or curl ... | sh. Without guardrails, the agent might comply.

Solution: AgentMD's isCommandSafe() blocks dangerous patterns. permissions.shell.default: deny with an explicit allowlist ensures only approved commands run. See Execution & Safety for the full blocked-pattern list.

Core Principles

1. Observable

OpenTelemetry (OTEL) — AgentMD plans OTEL export for traces and metrics. Use standardized semantic conventions for interoperability with Langfuse, Datadog, and other observability platforms.
Metrics — Track accuracy, bias, latency, success rate, and command-level pass/fail. The dashboard aggregates execution history, success rates, and audit logs.
Real-time investigation — Execution logs, status, and duration are available per run for debugging and root cause analysis.

2. Adaptive

Feedback loops — Use execution outcomes (success/failure, commands passed/failed) to refine agent configurations and permissions.
Human-in-the-loop — Use policy rules with approval: always for sensitive operations (deploy, migrate, production changes).
Iterative improvement — Adjust permissions.shell allowlists and guardrails based on observed failures.

3. Accountable

Audit trails — Execution history, audit logs, and policy results provide traceability.
Governance — Guardrails, permissions, and policies enforce boundaries. Cross-functional ownership and safety risk mitigation are supported through the Ops dashboard.
Deterministic workflows — AgentMD executes AGENTS.md as deterministic workflows. Commands are parsed, validated, and run in a defined order with explicit permission checks. This supports governance and reproducibility.

Deterministic Workflows

AgentMD is built on deterministic workflows for governance:

Commands are extracted from AGENTS.md in a predictable order.
Safety checks (isCommandSafe, isCommandAllowed) run before every execution.
Permission boundaries (allow/deny lists) are explicit and version-controlled.
Execution outcomes are deterministic given the same input and environment.

This contrasts with fully autonomous LLM-driven agents where behavior can vary between runs. Deterministic workflows make it easier to audit, debug, and comply with regulations.

Governance Checklist

Use permissions.shell.default: deny with explicit allowlists
Add guardrails in YAML frontmatter (e.g., "Never modify production")
Enable human-in-the-loop for sensitive operations
Review execution history and success rates regularly
Integrate with OTEL-compatible observability when available