Building Agents - Patterns & Best Practices

Design patterns, examples, and best practices for building robust goal-driven agents.

Prerequisites: Complete agent structure using hive-create.

Practical Example: Hybrid Workflow

How to build a node using both direct file writes and optional MCP validation:

# 1. WRITE TO FILE FIRST (Primary - makes it visible)
node_code = '''
search_node = NodeSpec(
    id="search-web",
    node_type="event_loop",
    input_keys=["query"],
    output_keys=["search_results"],
    system_prompt="Search the web for: {query}. Use web_search, then call set_output to store results.",
    tools=["web_search"],
)
'''

Edit(
    file_path="exports/research_agent/nodes/__init__.py",
    old_string="# Nodes will be added here",
    new_string=node_code
)

# 2. OPTIONALLY VALIDATE WITH MCP (Secondary - bookkeeping)
validation = mcp__agent-builder__test_node(
    node_id="search-web",
    test_input='{"query": "python tutorials"}',
    mock_llm_response='{"search_results": [...mock results...]}'
)

User experience:

• Immediately sees node in their editor (from step 1)
• Gets validation feedback (from step 2)
• Can edit the file directly if needed

Multi-Turn Interaction Patterns

For agents needing multi-turn conversations with users, use client_facing=True on event_loop nodes.

Client-Facing Nodes

A client-facing node streams LLM output to the user and blocks for user input between conversational turns. This replaces the old pause/resume pattern.

# Client-facing node with STEP 1/STEP 2 prompt pattern
intake_node = NodeSpec(
    id="intake",
    name="Intake",
    description="Gather requirements from the user",
    node_type="event_loop",
    client_facing=True,
    input_keys=["topic"],
    output_keys=["research_brief"],
    system_prompt="""\
You are an intake specialist.

**STEP 1 — Read and respond (text only, NO tool calls):**
1. Read the topic provided
2. If it's vague, ask 1-2 clarifying questions
3. If it's clear, confirm your understanding

**STEP 2 — After the user confirms, call set_output:**
- set_output("research_brief", "Clear description of what to research")
""",
)

# Internal node runs without user interaction
research_node = NodeSpec(
    id="research",
    name="Research",
    description="Search and analyze sources",
    node_type="event_loop",
    input_keys=["research_brief"],
    output_keys=["findings", "sources"],
    system_prompt="Research the topic using web_search and web_scrape...",
    tools=["web_search", "web_scrape", "load_data", "save_data"],
)

How it works:

• Client-facing nodes stream LLM text to the user and block for input after each response
• User input is injected via node.inject_event(text)
• When the LLM calls set_output to produce structured outputs, the judge evaluates and ACCEPTs
• Internal nodes (non-client-facing) run their entire loop without blocking
• set_output is a synthetic tool — a turn with only set_output calls (no real tools) triggers user input blocking

STEP 1/STEP 2 pattern: Always structure client-facing prompts with explicit phases. STEP 1 is text-only conversation. STEP 2 calls set_output after user confirmation. This prevents the LLM from calling set_output prematurely before the user responds.

When to Use client_facing

Legacy Note: The pause_nodes / entry_points pattern still works for backward compatibility but client_facing=True is preferred for new agents.

Edge-Based Routing and Feedback Loops

Conditional Edge Routing

Multiple conditional edges from the same source replace the old router node type. Each edge checks a condition on the node's output.

# Node with mutually exclusive outputs
review_node = NodeSpec(
    id="review",
    name="Review",
    node_type="event_loop",
    client_facing=True,
    output_keys=["approved_contacts", "redo_extraction"],
    nullable_output_keys=["approved_contacts", "redo_extraction"],
    max_node_visits=3,
    system_prompt="Present the contact list to the operator. If they approve, call set_output('approved_contacts', ...). If they want changes, call set_output('redo_extraction', 'true').",
)

# Forward edge (positive priority, evaluated first)
EdgeSpec(
    id="review-to-campaign",
    source="review",
    target="campaign-builder",
    condition=EdgeCondition.CONDITIONAL,
    condition_expr="output.get('approved_contacts') is not None",
    priority=1,
)

# Feedback edge (negative priority, evaluated after forward edges)
EdgeSpec(
    id="review-feedback",
    source="review",
    target="extractor",
    condition=EdgeCondition.CONDITIONAL,
    condition_expr="output.get('redo_extraction') is not None",
    priority=-1,
)

Key concepts:

• nullable_output_keys: Lists output keys that may remain unset. The node sets exactly one of the mutually exclusive keys per execution.
• max_node_visits: Must be >1 on the feedback target (extractor) so it can re-execute. Default is 1.
• priority: Positive = forward edge (evaluated first). Negative = feedback edge. The executor tries forward edges first; if none match, falls back to feedback edges.

Routing Decision Table

Judge Patterns

Core Principle: The judge is the SOLE mechanism for acceptance decisions. Never add ad-hoc framework gating to compensate for LLM behavior. If the LLM calls set_output prematurely, fix the system prompt or use a custom judge. Anti-patterns to avoid:

• Output rollback logic
• _user_has_responded flags
• Premature set_output rejection
• Interaction protocol injection into system prompts

Judges control when an event_loop node's loop exits. Choose based on validation needs.

Implicit Judge (Default)

When no judge is configured, the implicit judge ACCEPTs when:

• The LLM finishes its response with no tool calls
• All required output keys have been set via set_output

Best for simple nodes where "all outputs set" is sufficient validation.

SchemaJudge

Validates outputs against a Pydantic model. Use when you need structural validation.

from pydantic import BaseModel

class ScannerOutput(BaseModel):
    github_users: list[dict]  # Must be a list of user objects

class SchemaJudge:
    def __init__(self, output_model: type[BaseModel]):
        self._model = output_model

    async def evaluate(self, context: dict) -> JudgeVerdict:
        missing = context.get("missing_keys", [])
        if missing:
            return JudgeVerdict(
                action="RETRY",
                feedback=f"Missing output keys: {missing}. Use set_output to provide them.",
            )
        try:
            self._model.model_validate(context["output_accumulator"])
            return JudgeVerdict(action="ACCEPT")
        except ValidationError as e:
            return JudgeVerdict(action="RETRY", feedback=str(e))

When to Use Which Judge

Fan-Out / Fan-In (Parallel Execution)

Multiple ON_SUCCESS edges from the same source trigger parallel execution. All branches run concurrently via asyncio.gather().

# Scanner fans out to Profiler and Scorer in parallel
EdgeSpec(id="scanner-to-profiler", source="scanner", target="profiler",
         condition=EdgeCondition.ON_SUCCESS)
EdgeSpec(id="scanner-to-scorer", source="scanner", target="scorer",
         condition=EdgeCondition.ON_SUCCESS)

# Both fan in to Extractor
EdgeSpec(id="profiler-to-extractor", source="profiler", target="extractor",
         condition=EdgeCondition.ON_SUCCESS)
EdgeSpec(id="scorer-to-extractor", source="scorer", target="extractor",
         condition=EdgeCondition.ON_SUCCESS)

Requirements:

• Parallel event_loop nodes must have disjoint output_keys (no key written by both)
• Only one parallel branch may contain a client_facing node
• Fan-in node receives outputs from all completed branches in shared memory

Context Management Patterns

Tiered Compaction

EventLoopNode automatically manages context window usage with tiered compaction:

1. Pruning — Old tool results replaced with compact placeholders (zero-cost, no LLM call)
2. Normal compaction — LLM summarizes older messages
3. Aggressive compaction — Keeps only recent messages + summary
4. Emergency — Hard reset with tool history preservation

Spillover Pattern

The framework automatically truncates large tool results and saves full content to a spillover directory. The LLM receives a truncation message with instructions to use load_data to read the full result.

For explicit data management, use the data tools (real MCP tools, not synthetic):

# save_data, load_data, list_data_files, serve_file_to_user are real MCP tools
# data_dir is auto-injected by the framework — the LLM never sees it

# Saving large results
save_data(filename="sources.json", data=large_json_string)

# Reading with pagination (line-based offset/limit)
load_data(filename="sources.json", offset=0, limit=50)

# Listing available files
list_data_files()

# Serving a file to the user as a clickable link
serve_file_to_user(filename="report.html", label="Research Report")

Add data tools to nodes that handle large tool results:

research_node = NodeSpec(
    ...
    tools=["web_search", "web_scrape", "load_data", "save_data", "list_data_files"],
)

data_dir is a framework context parameter — auto-injected at call time. GraphExecutor.execute() sets it per-execution via ToolRegistry.set_execution_context(data_dir=...) (using contextvars for concurrency safety), ensuring it matches the session-scoped spillover directory.

Anti-Patterns

What NOT to Do

• Don't rely on export_graph — Write files immediately, not at end
• Don't hide code in session — Write to files as components are approved
• Don't wait to write files — Agent visible from first step
• Don't batch everything — Write incrementally, one component at a time
• Don't create too many thin nodes — Prefer fewer, richer nodes (see below)
• Don't add framework gating for LLM behavior — Fix prompts or use judges instead

Fewer, Richer Nodes

A common mistake is splitting work into too many small single-purpose nodes. Each node boundary requires serializing outputs, losing in-context information, and adding edge complexity.

Why fewer nodes are better:

• The LLM retains full context of its work within a single node
• A research node that searches, fetches, and analyzes keeps all source material in its conversation history
• Fewer edges means simpler graph and fewer failure points
• Data tools (save_data/load_data) handle context window limits within a single node

MCP Tools - Correct Usage

MCP tools OK for:

• test_node — Validate node configuration with mock inputs
• validate_graph — Check graph structure
• configure_loop — Set event loop parameters
• create_session — Track session state for bookkeeping

Just don't: Use MCP as the primary construction method or rely on export_graph

Error Handling Patterns

Graceful Failure with Fallback

edges = [
    # Success path
    EdgeSpec(id="api-success", source="api-call", target="process-results",
             condition=EdgeCondition.ON_SUCCESS),
    # Fallback on failure
    EdgeSpec(id="api-to-fallback", source="api-call", target="fallback-cache",
             condition=EdgeCondition.ON_FAILURE, priority=1),
    # Report if fallback also fails
    EdgeSpec(id="fallback-to-error", source="fallback-cache", target="report-error",
             condition=EdgeCondition.ON_FAILURE, priority=1),
]

Handoff to Testing

When agent is complete, transition to testing phase:

Pre-Testing Checklist

• Agent structure validates: uv run python -m agent_name validate
• All nodes defined in nodes/init.py
• All edges connect valid nodes with correct priorities
• Feedback edge targets have max_node_visits > 1
• Client-facing nodes have meaningful system prompts
• Agent can be imported: from exports.agent_name import default_agent

Related Skills

• hive-concepts — Fundamental concepts (node types, edges, event loop architecture)
• hive-create — Step-by-step building process
• hive-test — Test and validate agents
• hive — Complete workflow orchestrator

Remember: Agent is actively constructed, visible the whole time. No hidden state. No surprise exports. Just transparent, incremental file building.