{"id":743,"date":"2026-04-29T09:48:33","date_gmt":"2026-04-29T09:48:33","guid":{"rendered":"https:\/\/datascientists.info\/?p=743"},"modified":"2026-04-29T09:48:33","modified_gmt":"2026-04-29T09:48:33","slug":"cost-aware-agentic-workflows-with-pydanticai","status":"publish","type":"post","link":"https:\/\/datascientists.info\/index.php\/2026\/04\/29\/cost-aware-agentic-workflows-with-pydanticai\/","title":{"rendered":"Cost-Aware Agentic Workflows with PydanticAI"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Introduction: The Hidden Price of Autonomy<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>The Infinite Loop Problem:<\/strong> Why agentic workflows, especially those involving ReAct or recursive reasoning, are inherently more expensive than stateless RAG.<\/li>\n\n\n\n<li><strong>Economic Sustainability for SMBs:<\/strong> The necessity of moving away from move fast and break things towards structured governance for LLM spending.<\/li>\n\n\n\n<li><strong>The Solution:<\/strong> Implementing Cost Guardrails, a combination of pre-emptive token budgets and reactive human approval.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">The Architecture of a Cost Guardrail<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>The Interceptor Pattern:<\/strong> The architecture relies on middleware that wraps the LLM call, checking consumption before and after every request.<\/li>\n\n\n\n<li><strong>Primary Roles:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>PydanticAI:<\/strong> Enforces hard mathematical limits (total requests, total tokens) on the session.<\/li>\n\n\n\n<li><strong>LiteLLM:<\/strong> Provides semantic pricing intelligence, converting abstract token counts into actual currency (USD) in real-time.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Implementing Usage Limits with PydanticAI<\/h2>\n\n\n\n<p><strong>PydanticAI<\/strong> provides the primary library-level enforcement mechanism through its <code>UsageLimits<\/code> class.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: python; title: ; notranslate\" title=\"\">\nfrom pydantic_ai import Agent, RunContext\nfrom pydantic_ai.usage import UsageLimits\nfrom pydantic_ai.exceptions import UsageLimitExceeded\n\nfrom loguru import logger\n\n# 1. Define the agent\ninvestor_agent = Agent(\n    &#039;openai:gpt-5&#039;,\n    system_prompt=&quot;Analyze this stock portfolio. You may call tools multiple times.&quot;\n)\n\n# 2. Configure PydanticAI UsageLimits\nbudget_limits = UsageLimits(\n    request_limit=10,           # Max 10 LLM calls per run\n    request_tokens_limit=25000, # Max 25k prompt tokens\n    response_tokens_limit=10000 # Max 10k completion tokens\n)\n\ntry:\n    # 3. Apply limits to the run\n    result = investor_agent.run_sync(\n        &quot;Should I rebalance my tech portfolio?&quot;,\n        usage_limits=budget_limits\n    )\n    logger.info(f&quot;Success! Session Usage: {result.usage()}&quot;)\n\nexcept UsageLimitExceeded as e:\n    # 4. Catch the specific exception\n    # HIGHLIGHT: This triggers when ANY limit in UsageLimits is breached.\n    logger.info(f&quot;--- GUARDRAIL TRIGGERED ---&quot;)\n    logger.info(f&quot;Reason: {e}&quot;)\n    # Proceed to HITL Handling Section\n\n<\/pre><\/div>\n\n\n<h2 class=\"wp-block-heading\">Real-Time Cost Tracking with LiteLLM<\/h2>\n\n\n\n<p>While PydanticAI manages counts, LiteLLM converts those counts to dollars.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\nimport litellm\nfrom pydantic_ai import RunUsage\n\nfrom loguru import logger\n\ndef calculate_dollar_cost(usage: RunUsage, model_name: str) -&gt; float:\n    &quot;&quot;&quot;\n    Utility function to map PydanticAI usage data to LiteLLM pricing.\n    &quot;&quot;&quot;\n    # LiteLLM&#039;s implementation uses the same request\/response structure\n    cost_usd = litellm.completion_cost(\n        model=model_name,\n        prompt_tokens=usage.request_tokens,\n        completion_tokens=usage.response_tokens\n    )\n    return float(cost_usd)\n\n# conceptual example after an event:\n# total_usd = calculate_dollar_cost(result.usage(), &quot;gpt-4o&quot;)\n# logger.info(f&quot;Run Cost: ${total_usd:.4f}&quot;)\n\n<\/pre><\/div>\n\n\n<h2 class=\"wp-block-heading\">Detailed HITL Workflow: The Slack Intervention<\/h2>\n\n\n\n<p>For a SMB, a simple notification system like Slack is often the most effective way to implement Human-in-the-Loop (HITL) without building complex custom UIs.<\/p>\n\n\n\n<div class=\"wp-block-merpress-mermaidjs diagram-source-mermaid\"><pre class=\"mermaid\">graph TD\n    A[User Request] -->|Initiates Run| B(PydanticAI Agent);\n    B --> C{Pre-Call Check:\\nUsage Within limits?};\n    C -- Yes --> D[LiteLLM Proxy];\n    D -->|Calls LLM| E(LLM Provider);\n    E -->|Returns Response| D;\n    D --> F[PydanticAI Update Usage];\n    F -->|Result| B;\n    B -->|Final Answer| G[User];\n\n    C -- No (Threshold Exceeded) --> H[Raise UsageLimitExceeded];\n    H -->|Exception| I(HITL Intervention Handler);\n    I -->|Serializes State| J[Database];\n    I -->|Sends Approval Request| K[Slack API];\n    K --> L(Slack Workflow App);\n    L -->|Posts Message| M[Data Team Slack Channel];\n    M -->|Approves\/Denies| N[Human Reviewer];\n\n    N -- Denies --> O[Agent Terminates,\\nNotifies User];\n    N -- Approves --> P[Slack App Triggers\\nResume Endpoint];\n    P --> Q(Resume Script);\n    Q -->|Retrieves State| J;\n    Q -->|Injects Message History| R(New PydanticAI Agent Instance);\n    R -->|Continues Run| C;<\/pre><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">1. Exception Handling and State Serialization<\/h3>\n\n\n\n<p>When <code>UsageLimitExceeded<\/code> is caught, the agent is paused. Its entire state must be preserved to resume later.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n# (Continuing from Section III try\/except block)\nexcept UsageLimitExceeded as e:\n    # A. Access the partial result\/history from the exception\n    partial_history = e.partial_history  # Conceptual: The message chain so far\n\n    # B. Serialize State\n    session_id = str(uuid.uuid4())\n    save_agent_state_to_db(session_id, partial_history, current_usage=e.usage)\n\n    # C. Calculate LiteLLM Cost for the message\n    dollar_spent = calculate_dollar_cost(e.usage, &quot;gpt-4o&quot;)\n\n    # D. Trigger the Slack workflow\n    # trigger_slack_approval_request(session_id, dollar_spent, reason=str(e))\n\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\">2. The Slack Approval Workflow Structure<\/h3>\n\n\n\n<p>This process uses a configured <strong>Slack Workflow Builder<\/strong> app to handle the interaction.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>A. The Trigger:<\/strong> The Python script makes a POST request to a specialized webhook managed by the Slack Workflow App, passing the <code>session_id<\/code>, <code>dollar_spent<\/code>, and a snippet of the context.<\/li>\n\n\n\n<li><strong>B. The Slack Message:<\/strong> The Workflow App formats this into an interactive message posted to a #data-ops or #ai-approvals channel.<strong>\ud83d\udcb0 Agent Budget Alert \ud83d\udcb0<\/strong> <strong>Agent ID:<\/strong> <code>investor_agent_982<\/code> <strong>Status:<\/strong> PAUSED <strong>Reason:<\/strong> Token Limit Exceeded (Spent: <code>$0.12<\/code>)The agent needs another <strong>$0.20<\/strong> budget to continue. [Approve &amp; Add $0.20] [Deny &amp; Terminate]<\/li>\n\n\n\n<li><strong>C. The Human Action:<\/strong> A designated data team member reviews the message (or clicks a linked context log) and selects an option.<\/li>\n\n\n\n<li><strong>D. The Response Loop:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Slack sends the interaction payload back to your infrastructure (e.g., an AWS Lambda or FastAPI endpoint).<\/li>\n\n\n\n<li><strong>If Approved:<\/strong> The resumption script updates the DB, increases the budget context, and restarts the agent run (see below).<\/li>\n\n\n\n<li><strong>If Denied:<\/strong> The agent state in the DB is finalized, and a message is sent back to the user: &#8220;Agent run cancelled by supervisor.&#8221;<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">3. Resuming the Run<\/h3>\n\n\n\n<p>Resuming does not restart the task; it injects the previous history into a <em>new<\/em> agent instance.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: plain; title: ; notranslate\" title=\"\">\n# (FastAPI endpoint handling the &#039;Approve&#039; webhook from Slack)\ndef resume_agent_endpoint(payload: SlackApprovalPayload):\n    # 1. Verify approval and extract session_id\n    session_id = payload.session_id\n\n    # 2. Retrieve state from DB\n    original_history = get_agent_history_from_db(session_id)\n\n    # 3. Create a NEW agent instance with higher limits\n    extended_limits = UsageLimits(request_limit=15, total_tokens_limit=50000)\n\n    # 4. RUN AGAIN, INJECTING THE HISTORY\n    new_agent = Agent(&#039;openai:gpt-5&#039;)\n    new_result = new_agent.run_sync(\n        # We don&#039;t need the prompt again, the history contains it.\n        message_history=original_history,\n        usage_limits=extended_limits\n    )\n    return {&quot;status&quot;: &quot;resumed&quot;, &quot;final_result&quot;: new_result.data}\n\n<\/pre><\/div>\n\n\n<h2 class=\"wp-block-heading\">Best Practices for SMB Data Teams<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pre-Flight Estimation:<\/strong> Utilize <code>litellm.token_counter<\/code> on the initial prompt <em>before<\/em> starting the agent. If the starting prompt alone costs $0.10, perhaps GPT-4o is the wrong choice.<\/li>\n\n\n\n<li><strong>Model Routing by Cost:<\/strong> Implement PydanticAI logic that switches models dynamically. Use <strong>GPT-4o<\/strong> for strategic planning, but use <strong>GPT-4o-mini<\/strong> (via LiteLLM router) for repetitive data formatting or tool execution tasks to preserve budget.<\/li>\n\n\n\n<li><strong>Budget Tiers by Role:<\/strong> Define standard usage limit profiles based on context (e.g., <code>DEV_BUDGET<\/code>, <code>RESEARCH_BUDGET<\/code>, <code>CLIENT_BUDGET<\/code>).<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion: Governance as an Enabler<\/h2>\n\n\n\n<p>By combining PydanticAI&#8217;s native enforcement and LiteLLM&#8217;s pricing data, SMB data teams can deploy autonomous agents safely. This architecture moves beyond restriction and instead builds <em>economically sustainable<\/em> automation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction: The Hidden Price of Autonomy The Architecture of a Cost Guardrail Implementing Usage Limits with PydanticAI PydanticAI provides the primary library-level enforcement mechanism through its UsageLimits class. Real-Time Cost Tracking with LiteLLM While PydanticAI manages counts, LiteLLM converts those counts to dollars. Detailed HITL Workflow: The Slack Intervention For a SMB, a simple notification [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[137],"tags":[149,136,155,147],"ppma_author":[144,145],"class_list":["post-743","post","type-post","status-publish","format-standard","hentry","category-generative-ai","tag-agentic-ai","tag-genai","tag-litellm","tag-pydanticai","author-marc","author-saidah"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Cost-Aware Agentic Workflows with PydanticAI - DATA DO - \u30c7\u30fc\u30bf \u9053<\/title>\n<meta name=\"description\" content=\"Stop runaway LLM costs with cost-aware agentic workflows. 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