Agentic AI Implementation

Agentic AI Implementation for iSEEit

iSEEit is a sales execution and opportunity management software provider whose platform operationalizes the MEDDIC / MEDDPICC qualification methodology inside the seller’s daily workflow. Its customers are B2B revenue teams - sales representatives, managers, and revenue operations leaders, who depend on consistent deal qualification, accurate forecasting, and repeatable coaching across large, distributed sales organizations.

To remove the manual data-entry burden from sellers and make qualification continuous rather than periodic, iSEEit partnered with HabileLabs to design and deliver a secure, governed, multi-agent AI environment on AWS. The objective was to build agents that autonomously interpret unstructured sales signals, reason over them against the MEDDPICC framework, and take auditable actions such as updating qualification fields, surfacing deal risk, and recommending the next best action, while maintaining strict tenant isolation and enterprise data protection.

Challenges

Manual Qualification Burden

Sellers were expected to maintain structured MEDDPICC data by hand, resulting in incomplete records, stale deal information, and low methodology adoption.

Unstructured Signal Overload

Qualification evidence was scattered across call transcripts, emails, and meeting notes in formats no rules-based system could reliably interpret.

Multi-Step Reasoning

Determining whether an economic buyer is identified or a decision process is validated requires reasoning over evidence, not single-shot text generation.

Accuracy and Hallucination Risk

Any AI-generated change to a customer’s CRM system of record had to be evidence-grounded, explainable, and auditable.

Data Isolation and Security

As a multi-tenant SaaS provider handling commercially sensitive pipeline data, iSEEit required tenant-level isolation, encryption, and assurance that customer data is not used for model training.

Governance of Autonomous Agents

Observability, evaluation, versioning, and guardrails were required to safely operate agents that act rather than merely advise.

The Agentic AI Solution

HabileLabs designed and operationalized iSEEit’s agentic workloads on Amazon Bedrock, using a supervisor-and-specialist agent architecture with Anthropic Claude models as the reasoning engine.

Multi-Agent Orchestration and Reasoning

iSEEit’s qualification workloads run on Amazon Bedrock with Anthropic Claude models, routed per task - a higher-capability model for multi-step qualification reasoning and a faster model for extraction and classification.
A supervisor agent delegates to specialist agents for signal extraction, MEDDPICC qualification, deal risk detection, and next-best-action generation, coordinated by Amazon Bedrock Agents and AWS Step Functions for stateful, retryable workflows.
AWS Lambda exposes agent tools as discrete, least-privilege action groups so every agent capability is explicit and independently permissioned.

Grounding and Evidence-Based Retrieval

Amazon Bedrock Knowledge Bases provide retrieval-augmented generation over each tenant’s deal history and playbooks, with Amazon OpenSearch Serverless as the vector store and Amazon Titan Text Embeddings for embedding generation.
Amazon S3 stores transcripts, documents, and notes under tenant-scoped prefixes with server-side encryption, forming an immutable evidence layer that allows every generated value to be cited back to its source.

Safety, Guardrails, and Human Oversight

Amazon Bedrock Guardrails enforce denied topics, PII detection and redaction, and contextual grounding checks that block unsupported outputs before they reach a user or a CRM field.
Autonomy is tiered: analysis and recommendations run automatically, while write-back to the customer CRM requires human approval or a tenant-configured confidence threshold.
Amazon DynamoDB maintains agent state, action history, and an approval ledger, giving each tenant a complete record of what was proposed, accepted, and by whom.

Compliance and Security Management

AWS IAM session policies and per-tenant role assumption enforce isolation at the request level, ensuring no agent invocation can access data outside its tenant scope.
AWS KMS and AWS Secrets Manager protect keys and credentials, automate rotation, and ensure encryption in transit and at rest, while AWS CloudTrail and AWS Config deliver continuous auditing and traceability.

Evaluation and Observability

A golden-dataset evaluation harness scores agent outputs on qualification accuracy, grounding, and action appropriateness before any prompt, model, or tool change is promoted to production.
Amazon CloudWatch, Bedrock model invocation logging, and AWS X-Ray capture per-step traces, token consumption, latency, and guardrail interventions across the orchestration graph.

Event-Driven Processing and Integration

Amazon EventBridge and Amazon SQS trigger agent workflows the moment a new transcript, email, or CRM update arrives, making qualification continuous rather than a periodic batch exercise.
Amazon ECS on AWS Fargate and Amazon API Gateway host and secure the application and integration services, scaling on demand without capacity management.

Cost and Performance Optimization

Model routing, prompt caching, and context compaction direct each task to the smallest sufficient model and reduce repeated token spend across multi-step reasoning chains.
Token-budget ceilings per agent run and per-tenant cost attribution keep unit economics predictable as deal and transcript volumes grow.

Business Outcomes

Business Metric	Impact Achieved
Qualification Coverage	Opportunities with complete MEDDPICC data increased from 42% to 85% within 6 months.
Seller Productivity	Automated signal extraction and CRM updates removed approximately 3 hours per seller per week of manual data entry.
Forecast Accuracy	Improved forecast accuracy by 18% as qualification data became continuous and evidence-linked.
Qualification Accuracy	Agent-generated MEDDPICC assessments achieved 92% agreement with expert human review on the evaluation dataset.
Adoption	Weekly active usage of qualification features increased by 47% following launch.
Cost Optimization	Model routing, prompt caching, and token budgeting reduced inference cost per processed deal by 32%.
Governance	100% of autonomous actions were logged with source citations and a complete approval trail.

Why iSEEit Chose Agentic AI on AWS with HabileLabs?

Managed Foundation Models through Amazon Bedrock, providing frontier reasoning capability via a single secure API with no GPU infrastructure to operate.
Enterprise-Grade Data Protection with tenant isolation, encryption, and assurance that customer data is not used to train foundation models.
Guardrails and Grounding as First-Class Controls, making autonomous action safe enough to trust against a customer’s system of record.
Agentic Architecture Expertise in supervisor–specialist design, tool permissioning, evaluation harnesses, and human-in-the-loop controls.
Predictable Unit Economics through model routing, caching, and token governance aligned to a SaaS pricing model.
End-to-End Managed Experience - from use-case definition and architecture through implementation, evaluation, and steady-state operations.

Conclusion

By adopting an agentic AI architecture on AWS, iSEEit transformed sales qualification from a manual, self-reported exercise into a continuous, evidence-grounded system. HabileLabs’ implementation on Amazon Bedrock enabled multi-agent reasoning, guardrails, and human-in-the-loop governance for safe autonomous action. This foundation improved qualification coverage, forecast reliability, and seller productivity, while scaling securely across a growing multi-tenant customer base.