Leveraging Agentic AI for Strategic Alignment: A Series-A Startup’s Path to Innovation
Jacob Riedel, Murtaza Hakimi
January 31, 2025
•
5
min read
Background & Client Overview
A Series-A startup with over $12M in funding approached Malleable to explore embedding agentic AI capabilities into their core product. Their platform already helped teams of all sizes align on strategy, execute initiatives, and extract real-time insights at scale. Our goal was to rapidly prototype a system for building, testing, and deploying AI-driven conversational agents that could seamlessly integrate and collect strategic insights for their platform. Within a few months, Malleable delivered a containerized, cloud-agnostic solution supporting multiple language models (ChatGPT, Claude, etc.) and Slack integration for live agent interactions. This not only validated critical technical assumptions, but also provided the client with a clear AI product roadmap and a framework for quickly iterating on AI features.
Business Challenge
Fragmented Communication: Status updates were scattered across emails, spreadsheets, and multiple tools, making it difficult for executives to gain a unified view of ongoing projects.
Time-Consuming Manual Processes: Collecting and synthesizing data required significant effort from team members and managers, often leading to outdated or incomplete information.
Uncertain AI Integration Strategy: With limited internal AI expertise, the client was unsure how best to incorporate agentic AI without disrupting existing workflows or jeopardizing sensitive data.
Competitive Market Pressure: Other industry players had begun integrating AI-driven features, so the client needed to act swiftly to maintain a competitive edge.
Malleable.AI’s Solution
1. AI Agent Creation & Customization
Agent Architecture: Established a modular framework that allows the startup’s team to define knowledge bases, personalities, and conversation rules for each AI agent.
Custom Instructions: Introduced granular settings that guide each agent’s tone, style, and domain expertise, ensuring relevant and on-brand interactions.
2. Slack Integration & Agent Management
Custom Slack App: Implemented a Slack app to manage authentication, message routing, and real-time data extraction.
Management Console: Built an administrative interface where internal teams can configure agent behavior, track usage metrics, and quickly deploy updates.
3. Synthetic Conversations & Testing
Virtual Employees: Developed automated scripts representing hypothetical employees interacting with the AI agents on budget inquiries, OKRs, and project statuses.
Stress Testing: Covered a wide spectrum of scenarios, from routine requests to complex edge cases, to validate agent reliability and accuracy before going live.
4. Evaluation & Scoring Mechanism
Eval Agent: Designed a specialized AI evaluator that scores agent responses on clarity, correctness, tone, and compliance with company guidelines.
Continuous Feedback Loop: Integrated scoring data back into the system, prompting updates or alerts for human review when necessary.
5. Automated Analytics & Insights
Real-Time Dashboards: Aggregated conversation insights into dashboards for leadership, highlighting common issues, potential budgetary red flags, and overall agent efficacy.
Data-Driven Decision Support: Provided immediate visibility into employee concerns and resource utilization, enabling informed, agile decision-making.
Architecture and Engineering
The platform was implemented in two major components:
Frontend Development: Built with Next.js, Tailwind, Typescript, and React, the frontend offers a user-friendly interface for building and configuring agents, testing them, and monitoring performance.
Backend Development: Powered by Python, FastAPI, PostgreSQL, and the Langgraph SDK, the backend efficiently handles data processing, AI model integration and state management. Slack integration allowed the agents to be deployed directly as a Slack bot, making it easy for teams to interact with the agents in real-world scenarios.
Both the frontend and backend were containerized using Docker, making the platform agnostic to cloud providers and ensuring easy deployment, scalability, and easy adoption of future advancements in AI technology
Results & Impact
Rapid AI Agent Creation & Customization
The modular framework allowed the startup to spin up and fine-tune new agents efficiently, adapting agent “personalities” and knowledge sources to specific business functions and policies.
Accelerated QA & Time-to-Market
Synthetic testing drastically cut the time required for quality assurance, detecting edge-case issues early and ensuring well-trained agents upon launch.
Seamless Employee Experience
By integrating natively with Slack, leaders could request budget updates or OKR status on demand, reducing email overhead and boosting productivity.
Continuous Improvement via Eval Agent
Real-time scoring delivered actionable feedback, enabling ongoing refinements to conversation quality and policy adherence.
Leadership Insights & Strategic Alignment
Aggregated conversation data provided instantaneous visibility into departmental and cross-functional challenges, helping executives make faster, more data-informed decisions.
First AI Feature
Kickstarted the series-A company’s AI journey, pleased investors, and has helped the whole team see the value of AI and how it can be integrated in new features to come.
Key Takeaways
AI Integration is a Journey, Not a One-Time Task: Successful AI adoption requires continuous iteration and adaptation. The POC served as a stepping stone for refining the client’s AI product roadmap.
Flexibility is Key: A platform that allows for easy integration with emerging AI technologies ensures that businesses remain agile and competitive in a rapidly changing landscape.
Real-Time Feedback Drives Better Decisions: By providing both synthetic and human interaction testing, we gave the client actionable insights into their agents’ performance, ensuring the solution was aligned with user needs from the outset.
