MightyBot vs Amazon Bedrock

Amazon Bedrock AgentCore launched as AWS's agentic platform: Agent Registry for managed catalogs, Quality Evaluations for automated testing, Policy Controls for natural language boundaries. Nova 2 models (Lite, Pro, Sonic) provide fast, cost-effective reasoning. 100+ models via Marketplace. Significant infrastructure. But AgentCore orchestrates at runtime. MightyBot compiles execution plans upfront. The difference: AgentCore decides what to do as agents run. MightyBot plans the entire path before starting. One orchestrates. The other executes right the first time.

AWS Textract extracts text, tables, and forms from documents with high accuracy. It's widely used, well-priced, and integrates cleanly with S3 and Lambda. Textract is extraction. MightyBot's document intelligence is a pipeline: classify incoming packet type, split multi-document bundles, extract with confidence routing, normalize to canonical dictionaries, and reconcile across sources. Does the Textract-extracted invoice amount match the approved budget line item? Does the appraisal align with the purchase contract? Every field carries an evidence pointer — document name, page, character offset, confidence score. Textract tells you what's in the document. MightyBot tells you what it means and whether it's consistent.

AgentCore Policy uses natural language to set boundaries for agent actions. Cross-account Guardrails (April 2026) enforce safeguards across AWS Organizations. Content moderation, PII redaction, hallucination detection. These are runtime controls. MightyBot's policy engine is business logic: 'if DTI exceeds 43%, escalate with supporting documents.' Version it in git. Backtest against historical transactions. Deploy same-day. Roll back if outcomes drift. AgentCore Policy prevents bad actions. A versioned policy engine defines what decisions are correct.

AWS CloudTrail logs every API call to Bedrock — which model was invoked, which prompts were sent, which actions were taken. This is security and operational auditing. Regulatory compliance in financial services requires decision auditing. An auditor asks 'why did you approve this construction draw, and what documents supported that decision?' CloudTrail shows you called claude-3-5-sonnet-20241022 with a 4,200-token prompt. It doesn't link the decision to the specific policy version, the specific extracted field value, and the specific evidence source. MightyBot generates why-trails as decisions happen — not reconstructed logs, but forward-generated decision records exportable to S3, Snowflake, or Iceberg.