The Tool Desk
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Why contract portfolios need two ways to answer questions
“Which vendor are we spending the most with?” and “Which contracts are about to expire?” require comparisons across a collection. To answer them reliably, a system needs relevant facts from the full portfolio in a form that can be filtered, counted, and aggregated.
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“What are the payment terms in the AnyCompany contract?” is different: it asks about a specific source document. Retrieval can find relevant passages in that contract, while structured records support portfolio-wide calculations. Neither approach replaces the other.
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AWS’s Machine Learning Blog article, published September 29, 2026, frames the design choice this way: “A better prompt won’t fix this. A different architecture will.” The point is architectural: retrieval can help with a focused lookup, but selecting a limited number of semantically relevant document chunks does not ensure that every contract needed for a portfolio total is represented.
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How the AWS extraction and verification pipeline works
The September 2026 example turns contract PDFs into structured records while retaining the source files for traceability and document-specific questions.
- Preserve the PDFs in Amazon S3. Keeping the originals gives reviewers a source to check when an extracted field is unclear and leaves the documents available for focused retrieval.
- Extract a defined schema. An extraction agent reads each PDF and returns eight fields, each with a confidence score. The example uses a Claude Sonnet-series model with native PDF reading. Model availability and suitable choices can vary by AWS Region, so check current regional options for a deployment.
- Ask a separate agent to verify the fields. A verification agent reads the same contract and checks the extracted values. AWS’s rationale is that a different model can provide an independent perspective. Agreement is not proof of correctness, and disagreement is a signal to investigate—not evidence that either model is right.
- Run a narrow signature check when needed. The example invokes Amazon Textract only when the two agents disagree about the
is_signedfield. The AWS article notes that a model may mistake an empty signature line for a signature. This is a targeted visual check for signature presence, not a general solution to interpreting legal meaning. - Store verified fields in Aurora PostgreSQL. Structured records can be queried across the portfolio for totals, filters, and comparisons. Keep the original documents available as well; a database row is not a substitute for the contract text.
- Offer both analytics and document lookup. The example uses Amazon Quick for analytics and natural-language queries. Portfolio questions should use the structured records; questions about a specific clause should be grounded in the relevant source document.
What AgentCore contributes—and what it does not
AWS describes AgentCore as modular infrastructure for building and operating agents with multiple frameworks and foundation models. Its components address different operational needs:
- Runtime hosts and scales agent workloads.
- Identity handles agent identity and access.
- Gateway makes APIs and tools available to agents.
- Code Interpreter provides sandboxed code execution.
- Observability supports tracing, auditing intermediate outputs, and debugging workflow performance.
- Harness, Memory, and Browser are additional capabilities listed in AWS documentation.
These are platform functions, not guarantees that an extraction is correct, that document access is authorized, or that a deployment meets legal or regulatory obligations. Those outcomes depend on the system’s configuration, data handling, access policies, evaluation, and human oversight.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchHow this differs from a team of legal, risk, and compliance agents
AWS’s January 27, 2026 contract-management guide describes a separate multi-agent pattern. A collaboration agent coordinates specialized agents: legal extracts parties, terms, and obligations; risk assesses financial and operational exposure; compliance evaluates regulatory requirements. The coordinator consolidates their findings. The guide connects Quick Suite workflows and data access with AgentCore agents, S3 documents, and Redshift structured data.
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- Understand how contract provisions work
- Adapt reliable drafting precedents
- Avoid drafting errors, omissions, and ambiguities
- Make contracts more user-friendly
- Build flexibility into contracts without compromising precision
This is not the same design as having one model extract fields and another independently verify them. The two approaches divide work differently:
| Pattern | How work is divided | Primary purpose |
|---|---|---|
| Extraction plus verification | One agent extracts defined fields; another checks those values against the contract. | Identify possible field-level errors and route disagreements for review. |
| Function-specialized agents | Legal, risk, and compliance agents analyze the contract from different business perspectives; a coordinator consolidates findings. | Organize distinct kinds of contract-management work. |
AWS’s sample repository shows a broader implementation example with agents for contract Q&A, administration, analytics, contract creation, compliance, and renewals. It describes role-based access and a stack that includes AgentCore Runtime, Bedrock, Knowledge Bases, OpenSearch Serverless, S3, Cognito, and CloudFront. Treat this as sample code and a design reference, not a guarantee about a deployed system.
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What the published evaluation does—and does not—show
AWS reports hand-labeling eight fields across 20 contracts, or 160 labeled field values, to compare extractor and verifier combinations. That is a small, directional evaluation—not an exhaustive study, a population statistic, or an independently validated performance result. AWS also notes that results can vary with contract format and field complexity.
For a real deployment, build a representative evaluation set from the contracts and fields that matter to your organization. Establish field-level ground truth, define which disagreements require human review, and measure results against explicit success criteria. A model’s confidence score can help prioritize review, but it should not be treated as a substitute for measured performance or source checking.
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Practical decisions before implementation
- Separate query types. Use retrieval for focused questions about an agreement and structured records for totals or comparisons across the collection.
- Decide whether verification is worth the added step. A second agent creates a disagreement signal, but its value must be assessed against labeled examples and the cost of review.
- Keep deterministic checks narrow. A visual signature-presence check addresses that visual question; it does not establish whether a contract is enforceable or whether a clause has a particular legal effect.
- Set access and retention rules. Restrict who and which agents can access contracts, decide how long source files and extracted records are retained, and audit the workflow’s intermediate outputs. Security features do not make every deployment compliant by default.
- Plan human escalation. Define who reviews missing, conflicting, or low-confidence values and how corrections are recorded against the source.
- Account for deployment costs. AWS services can incur infrastructure costs; estimate the services used and check current pricing before deployment.
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