The future of sales and marketing lies in intelligent, autonomous systems that don’t just automate tasks but adapt, collaborate, and deliver measurable value. That’s why we’re designing an agentic framework, a modular, scalable architecture where AI agents are reusable, easy to implement, and configurable to meet diverse needs. Whether it is uncovering market insights or streamlining pipelines, our goal is to provide tools for sales teams that think and act like their best human counterparts. In this post, we’ll walk you through the principles behind this framework, break down its structure, and share how we’re tackling the challenges of performance, privacy, and collaboration.

Why an Agentic Framework?

Traditional automation scripts are rigid—one-trick ponies that break when the environment shifts. AI agents, on the other hand, can reason, learn, and adjust. But to make them practical for everyday use, they need to be:

• Pre-made yet configurable: Ready out of the box but adaptable to specific goals.
• Integrity-Driven: Isolated and secure, ensuring no cross-contamination between users or processes.
• Maintainable: Easy to update without breaking existing workflows.
• Performant: Fast enough to keep up with real-time demands.
• Reliable: Consistent even under load or failure.
• Private: Protecting sensitive sales data at every step.

These aren’t just nice-to-haves, they are the core features of a system that companies/organizations can trust. So, how do we get there?

The Four Agent Groups

We see sales as a multifaceted ecosystem that our agents should reflect on. We’ve categorized them into four distinct groups, each addressing a critical piece of the sales puzzle:

1. Planning: Agents like Market Research or Ideal Customer Profile (ICP) Analytics dig into data to uncover opportunities. For example, an ICP Analytics agent might scour market trends and customer data to define who’s most likely to buy your product—saving weeks of manual analysis.
2. Sales: Agents like Pipeline Sourcing focus on execution. This agent could crawl the web, company pages, or tools data to identify high-potential leads, feeding your pipeline with precision.
3. Marketing: Agents like Marketing Emails handle outreach. Imagine an agent that drafts personalized emails, schedules them based on prospect behavior, and tracks responses—all without human hand-holding.
4. Operations & Management: Agents like Activity Planner keep the machine running. This agent might prioritize tasks, schedule follow-ups, and assign workloads across your team, ensuring nothing slips through the cracks.

These groups aren’t silos—they’re building blocks. The magic happens when they collaborate, which is where our framework design comes in.

Anatomy of an Agent

Before we architect the system, let’s dissect what makes an agent tick. Every agent in our framework follows a standardized structure:

• Role: Defines its identity—e.g., ICP Analyst.
• Focus: Its mission and cadence—e.g., “Find ICPs for a product, updated quarterly.”
• Tasks: The steps it takes—e.g., 1) Collect source data, 2) Profile ICPs, 3) Save to a database.
• Tools: Functions it uses—e.g., `ask_user()` for input, `fetch_company_info()` for data retrieval, `write_to_db()` for storage.
• Guardrails: Constraints to keep it in check—e.g., “Only analyze companies in North America.”
• Memory: Short-term (current state, like “I’m on step 2”) and long-term (historical ICPs or market insights).

This structure ensures agents are modular yet purpose-built. But how do they operate? That’s where design patterns come into play.

Agent Design Patterns

We draw from five key patterns to give agents their intelligence:

• Reflection: Agents self-critique and refine their output. An ICP Analyst might re-evaluate its profiling if the results don’t align with market data.
• Tool Use: Agents expand their reach with external tools. A Pipeline Sourcing agent might tap APIs like LinkedIn or Crunchbase to enrich lead data.
• ReAct: Agents combine reasoning and action to solve problems dynamically. A customer support agent might analyze a query, reason through potential solutions, and act by drafting a response or escalating the issue—all in real-time.
• Planning: Agents break complex goals into steps. An Activity Planner might sequence weekly tasks, adjusting based on team availability.
• Multi-Agent Collaboration: Specialized agents team up. Imagine a Market Research agent feeding insights to an ICP Analyst, who then hands off to a Pipeline Sourcing agent—a seamless relay.

These patterns make agents versatile, but the real challenge is deploying them in a way that meets our core criteria.

Building the Framework: Meeting the Criteria

Here’s how we satisfy our core criteria with a practical, sales-ready architecture:

1. Pre-made yet Configurable

Agents are packaged as Docker containers, pre-built with a standard template:

agent:

 role: DataParser

 goal: Parse raw data

 tasks: [parse_data]

 tools: [regex]

 guardrails: [max_input_size=1MB]

 topics: [user_a.parser_tasks]

A Configuration Manager—a service running alongside our backoffice—reads the YAML blueprint, spins up a container, and injects the config. Users tweak roles, tools, or guardrails via a simple interface, ensuring flexibility without coding. For example, a user could redefine a Pipeline Sourcing agent to target a new industry by updating the config.

2. Integrity

Docker containers isolate each agent, sandboxing memory and processes. User A’s Market Research agent can’t access User B’s data or crash their Pipeline Sourcing agent. This per-user containerization ensures failures are contained and sensitive sales data (e.g., lead lists) stays secure within its scope.

3. Maintainability

Versioned tools and configs keep agents manageable. The Configuration Manager logs every deployed agent’s blueprint—role, tools, version—and updates containers when new tool versions (e.g., an improved `fetch_company_info()`) are released. Rolling updates happen without downtime, and past configs serve as an audit trail for troubleshooting.

4. Execution Performance

Speed matters in sales, so we optimize at every layer:

• Lightweight Containers: Docker minimizes resource overhead, letting dozens of agents run on a single server.
• Asynchronous Messaging: RabbitMQ handles inter-agent communication, and queuing tasks so agents don’t block (e.g., ICP Analyst notifies Pipeline Sourcing without waiting).
• Task Offloading: Heavy lifting (e.g., data profiling) shifts to scalable backends like Qdrant or Postgres, keeping agents responsive.
• Caching: Redis caches frequent lookups (e.g., company data), cutting redundant calls.

5. Reliability

Consul acts as our service discovery backbone. Agents register their IP:port on startup (e.g., `172.17.0.2:8080` for a DataParser), and Consul’s health checks (via a `/health` endpoint) ensure only live agents are addressable. Consul updates dynamically if a container restarts or scales, preventing failed messages or dead-end requests.

6. Privacy

Privacy is crucial to get right. Consul tags agents by users (e.g., `user=user_a`), restricting discovery to same-user agents. RabbitMQ reinforces this with virtual hosts (e.g., `/user_a`) or topic prefixes (e.g., `user_a.parser_tasks`), ensuring Send Marketing Emails for User A never leaks to User B. Memory—stored in Redis or Postgres—is scoped to user-specific keys or schemas (e.g., `user_a:icp_records`).

Why This Matters for Sales & Marketing

Sales thrive on speed, insight, and execution. Our framework turns fragmented processes into a cohesive AI-driven engine. A Market Research agent spots a trend, an ICP Analyst profiles it, and a Pipeline Sourcing agent fills your CRM—all in sync, all configurable. It’s a framework/setup that scales.

What’s Next?

We’re testing this framework in real sales scenarios, fine-tuning performance, and evolving multi-agent collaboration further.