{"slug": "getting-agents-to-stop-assuming-what-a-first-aws-agent-workflow-reveals-about", "title": "Getting Agents to Stop Assuming: What a First AWS Agent Workflow Reveals About Constraint Design", "summary": "A developer building an AWS Bedrock agent for customer support found that the agent's 17% failure rate stemmed from it confidently creating bug tickets with missing required fields. The fix was not better prompting but an architectural change: inserting a deterministic validation gate between information gathering and ticket creation, moving the decision out of the model's control. The developer also implemented refusal patterns to route requests requiring human judgment away from the agent.", "body_md": "The most common failure mode in agent workflows is not a timeout or a bad API call. It is the agent confidently doing the wrong thing because it filled in missing information with a plausible guess.\n\nA practitioner building their first AWS Bedrock agent for customer support hit this exact problem. The agent was supposed to collect three pieces of information before creating a bug ticket: problem description, reproduction steps, and environment details. In two test cases, the agent understood the intent and created the ticket anyway, even though one required field was missing. The evaluation correctness score was 0.83, which sounds decent until you realize the 17% failure rate came from the agent assuming it had enough context when it did not.\n\nThis is not a prompt engineering problem. It is an architecture problem. The agent needs validation gates that block execution when constraints are not met, not prompts that politely ask the model to be careful.\n\nLLMs are trained to be helpful. When you ask a question, they generate an answer. When you describe a workflow, they try to complete it. This behavior breaks down in agentic systems where partial information should trigger a refusal, not a best guess.\n\nIn the bug ticket scenario, the agent had three options:\n\nThe agent chose option 1 because the model understood the user's intent and the prompt did not enforce a hard boundary. The fix is not better prompting. The fix is moving validation out of the model's decision space entirely.\n\nBedrock AgentCore orchestrates multi-step workflows using action groups, which are Lambda functions the agent can invoke. The naive approach is to let the agent decide when it has enough information to call the \"create ticket\" action. The safer approach is to split the workflow into two stages with a validation gate in between.\n\n**Stage 1: Information Gathering**\n\nThe agent collects user input and stores it in session state. No ticket creation happens here. The action group for this stage only writes to DynamoDB or session attributes.\n\n**Validation Gate**\n\nA separate Lambda function checks whether all required fields are present. This function does not use the LLM. It is a schema validator:\n\n``` python\ndef validate_bug_report(event):\n    required_fields = ['description', 'reproduction_steps', 'environment']\n    collected = event.get('sessionAttributes', {})\n\n    missing = [f for f in required_fields if not collected.get(f)]\n\n    if missing:\n        return {\n            'status': 'incomplete',\n            'missing_fields': missing,\n            'next_action': 'prompt_user'\n        }\n\n    return {\n        'status': 'complete',\n        'next_action': 'create_ticket'\n    }\n```\n\n**Stage 2: Ticket Creation**\n\nThe agent can only invoke the ticket creation action if the validation gate returns `status: complete`\n\n. If the gate returns `incomplete`\n\n, the orchestration flow routes back to the information gathering stage with a specific prompt about the missing fields.\n\nThis architecture removes the decision from the model. The agent cannot guess its way past the gate.\n\nValidation gates handle missing information. Refusal patterns handle requests the agent should not attempt at all.\n\nIn the customer support agent, three request types were defined:\n\nThe refusal logic sits in the action group router. Before the agent invokes any action, a classifier function (which can be a lightweight model or rule-based logic) determines the request type. If the type is \"escalate,\" the agent does not try to answer. It returns a structured response that triggers a handoff.\n\n``` python\ndef route_request(user_input, session_state):\n    intent = classify_intent(user_input)  # Lightweight classifier\n\n    if intent == 'escalate':\n        return {\n            'action': 'create_handoff_ticket',\n            'reason': 'requires_human_judgment',\n            'context': session_state\n        }\n\n    if intent == 'bug_report':\n        return {'action': 'start_bug_collection'}\n\n    if intent == 'faq':\n        return {'action': 'query_knowledge_base'}\n```\n\nThe key is that the refusal decision happens before the agent enters a reasoning loop. The model does not get a chance to be helpful in a way that breaks policy.\n\nThe hardest failures to catch are the ones where the agent completes the workflow but with incorrect assumptions baked in. The ticket gets created, the user gets a confirmation, and nobody notices until a human reviews the ticket and realizes the reproduction steps are missing.\n\nYou need three observability hooks:\n\n**1. Session State Snapshots**\n\nLog the full session state before and after every action group invocation. This lets you replay the workflow and see exactly what information the agent had at each decision point.\n\n**2. Validation Gate Metrics**\n\nTrack how often the validation gate returns `incomplete`\n\nand which fields are most commonly missing. If 40% of bug reports are missing environment details, your information gathering prompt needs work.\n\n**3. Assumption Flags**\n\nInstrument your action groups to log when they receive partial data. Even if the validation gate passes, the action group itself should check its inputs and flag any fields that are present but suspiciously generic (like \"unknown\" or \"not specified\"). These flags do not block execution, but they surface in your monitoring dashboard.\n\n| Approach | Failure Mode | Observability | Testability |\n|---|---|---|---|\n| Prompt-based (\"Please ensure you have X, Y, Z before proceeding\") | Model ignores instruction under ambiguous input | Requires LLM trace analysis to see why it proceeded | Hard to write deterministic tests |\n| Gate-based (schema validation in Lambda before action) | Gate logic bug (rare, deterministic) | Clear pass/fail in CloudWatch logs | Standard unit tests on validation function |\n| Hybrid (prompt + gate) | Redundant, but catches model drift | Both LLM trace and gate logs | Best coverage, higher complexity |\n\nThe hybrid approach is overkill for most workflows. Start with gate-based validation. Add prompt-level guidance only if you see the model repeatedly trying to invoke actions it should know are blocked.\n\nYou cannot test agent reliability by only checking happy paths. You need a test suite that explicitly tries to trick the agent into acting on incomplete information.\n\n**Test Case 1: Missing Required Field**\n\nInput: \"The app crashes when I open it.\"\n\nExpected: Agent asks for reproduction steps and environment.\n\nFailure: Agent creates ticket with only description.\n\n**Test Case 2: Ambiguous Intent**\n\nInput: \"Can you help me with my account?\"\n\nExpected: Agent escalates to human (account changes require verification).\n\nFailure: Agent tries to answer from FAQ or asks clarifying questions it cannot act on.\n\n**Test Case 3: Partial Information with Confidence**\n\nInput: \"I am on iOS and the app crashes. I think it is a memory issue.\"\n\nExpected: Agent asks for reproduction steps (the user's theory is not a substitute for steps).\n\nFailure: Agent creates ticket with user's theory in the reproduction steps field.\n\nRun these tests after every prompt change and after every action group update. If your correctness score drops, check whether the failures are in the \"agent refused correctly\" category or the \"agent assumed incorrectly\" category. The first is usually acceptable. The second is not.\n\nThe customer support agent used:\n\nSession state is the critical piece. If you lose session state between turns, the agent cannot track what information it has already collected. DynamoDB is the standard choice here because it integrates natively with Bedrock session attributes and supports TTL for automatic cleanup.\n\nThe validation gate Lambda should be stateless. It reads from session attributes, runs schema validation, and returns a routing decision. No side effects. This makes it easy to test in isolation and easy to replace if you need to change validation logic.\n\n**1. Session State Drift**\n\nIf the agent updates session state in the prompt but the Lambda function reads from a stale snapshot, the validation gate might pass when it should fail. Always write session state updates synchronously and confirm the write before proceeding to the next turn.\n\n**2. Overly Strict Validation**\n\nIf your gate requires exact field names or specific formats, minor variations in how the agent structures the data will cause false negatives. Use flexible schema validation (check for presence and type, not exact keys).\n\n**3. Escalation Loops**\n\nIf the refusal pattern triggers too easily, users get stuck in a loop where the agent keeps saying \"I cannot help with that\" without explaining what it can help with. Always pair a refusal with a concrete next step (either a clarifying question or a handoff with context).\n\n**Use gate-based validation when:**\n\n**Avoid gate-based validation when:**\n\nThe lesson from this first agent build is simple: if you want an agent to admit ignorance, do not ask it politely. Build a gate that blocks execution when constraints are not met. The model will thank you by not having to guess.", "url": "https://wpnews.pro/news/getting-agents-to-stop-assuming-what-a-first-aws-agent-workflow-reveals-about", "canonical_source": "https://dev.to/mech_app_ai/getting-agents-to-stop-assuming-what-a-first-aws-agent-workflow-reveals-about-constraint-design-4o2l", "published_at": "2026-09-03 20:05:34+00:00", "updated_at": "2026-09-03 20:25:19.443281+00:00", "lang": "en", "topics": ["ai-agents", "developer-tools", "ai-infrastructure"], "entities": ["AWS Bedrock", "AgentCore", "Lambda", "DynamoDB"], "alternates": {"html": "https://wpnews.pro/news/getting-agents-to-stop-assuming-what-a-first-aws-agent-workflow-reveals-about", "markdown": "https://wpnews.pro/news/getting-agents-to-stop-assuming-what-a-first-aws-agent-workflow-reveals-about.md", "text": "https://wpnews.pro/news/getting-agents-to-stop-assuming-what-a-first-aws-agent-workflow-reveals-about.txt", "jsonld": "https://wpnews.pro/news/getting-agents-to-stop-assuming-what-a-first-aws-agent-workflow-reveals-about.jsonld"}}