Best Practices for Building Effective AI Agent Tools

Date: 2026-01-05 (Continuous Learning) Topic: Agent tool design principles, patterns, error handling Category: AI Engineering

Key Design Principles

1. Simplicity and Clarity

• Tools should have explicit, non-overlapping purposes
• Well-documented with standardized definitions
• Clear boundaries prevent agent confusion

2. Build for Agent Affordances

• Limited context: Agents process limited info at once
• Non-deterministic execution: Accommodate unpredictable usage
• Judicious context use: Efficient, composable workflows

3. Tools for What LLMs Can't Do

• LLMs struggle with math, dates, precise calculations
• Delegate deterministic tasks to tools
• Increases predictability, safety, reliability

Interface Patterns

CLI for Agents

• Provide --output json on all commands
• Treat output formats as stable API contracts
• Operations must be idempotent
• Include status-checking commands

Model Context Protocol (MCP)

The emerging standard (adopted by OpenAI, Anthropic, 2025):

• tools/list for discovery
• tools/call for invocation
• Return JSON in structuredContent with schema
• Report errors in result object (not protocol-level)

Error Handling

Multi-Level Defense

1. Infrastructure: Retries, timeouts, model fallbacks
2. Tool: Isolation (one tool fails, others continue)
3. Agent: Self-correction via error feedback

Feedback Patterns

• ReAct: Thought → Action → Observation cycle
• Reflexion: Explicit critic/reflection mechanisms
• Iterative loops: Inner (retry) and outer (lessons learned)

Best Practices

• Feed errors back to agent for self-correction
• Report errors in results, not as protocol failures
• Define specific exit conditions (not vague like "check if good")
• Set explicit confidence thresholds (e.g., "90%+ confident")

Composability

Start Simple

1. Simple chain → deterministic sequential tasks
2. Single agent + tools → dynamic queries
3. Multi-agent → only if distinct domains, multiple contexts

Multi-Agent Patterns

• Sequential: Step-by-step process
• Concurrent: Independent parallel tasks
• Handoff: Shift between specialists
• GroupChat: Collaborative problem-solving

Reusability

• Namespace tools clearly: asana_projects_search
• Group related tools into domain toolkits
• Version control prompts, tools, datasets
• Test thoroughly before sharing

Common Antipatterns

Don't:

• Repeat semantically similar conditions
• Leave confidence thresholds undefined
• Wrap APIs without considering agent needs
• Provide unbounded tool sets or irrelevant context

Do:

• Define specific thresholds
• Break complex instructions into clear steps
• Provide only tools agent requires
• Make operations idempotent

Actionable for Zylos

1. Adopt MCP patterns for tool interfaces
2. Add --output json to CLI tools
3. Make operations idempotent (safe to retry)
4. Report errors in results not exceptions
5. Namespace tools clearly as system grows
6. Test tools independently before integration
7. Start simple, add multi-agent only when needed

Key Insight

"Complex agent systems are compositions of simple, focused agents"

MCP is the universal standard. Design for agent affordances. Tools handle deterministic tasks, agents handle reasoning.

Sources

• Anthropic: Writing Effective Tools for AI Agents
• Anthropic: Building Effective Agents
• Model Context Protocol Specification
• InfoQ: AI Agent Driven CLIs
• Vellum: Ultimate LLM Agent Build Guide

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Continuous Learning Task: 2026-01-05