--- title: "Agentic AI built for production, not demos" url: "https://bravr.ai/labs/agentic" description: "Agentic AI systems that actually do the work. Move beyond simple LLM wrappers to production-ready agentic AI for UK businesses." --- # Agentic AI built for production, not demos ## The next evolution of AI implementation. Move beyond the chatbot. We build agentic systems that coordinate complex workflows, manage state, and actually do the work. Production-ready agency, not a GPT wrapper. / 01 — THE CHALLENGE ## The Problem Teams adopting AI—whether just getting started or already building advanced multi-agent systems—tend to run into the same structural limits: single-model constraints, lack of persistent memory, and no reliable way to coordinate complex, multi-step workflows. A single general-purpose model can stretch across roles like project manager, developer, strategist, or analyst, but not without tradeoffs in context, consistency, or output quality. Without continuity between sessions, progress is fragile. Conversations reset, context disappears, and prior decisions or insights aren’t retained. For beginners, this creates friction and repetition. For more advanced users, it becomes a bottleneck that limits scalability and automation. In both cases, the absence of shared memory and coordination makes it difficult to build systems that evolve, collaborate, and improve over time. ### Generalist Constraints Single models lack the deep specialisation needed for domain-specific tasks. Jack of all trades, master of none. ### No Coordination Complex workflows need role specialisation and delegation. One model can't be CFO and CTO simultaneously. ### No Memory Persistence Single-pass AI forgets what happened yesterday. Teams need continuity, context, and institutional knowledge across sessions. / 02 — OUR APPROACH ## The Solution A single AI can answer questions. But real business operations require coordination, delegation, and specialisation, the same way a human team works. We built Agentic to move beyond chat: a team of AI agents that each have a role, a memory, and access to tools, working together via a platform teams already use daily. Each agent has a defined role, independent persistent memory, access to real tools through MCP integration, and the ability to communicate with the team through a central orchestrator. The result is an AI team that handles complex projects from research through execution with minimal human intervention. FEATURES ### Multi-Agent Architecture Specialised agents with defined roles that collaborate on complex workflows ### Persistent Memory Long-term recall across sessions ### MCP Tool Integration Web search, JIRA, Google Tasks, and more give agents real-world capabilities / 03 — THE TEAM ## Meet the Agent Team Each agent has a distinct personality, domain expertise, and set of tools. The orchestrator routes work to the right specialist based on the task at hand. Here’s who’s on the team. ### Laila — PM and Orchestrator The central coordinator. Laila receives user requests, breaks them into tasks, routes work to the right specialist, and synthesises results. She manages project tracking and ensures the team stays aligned. ### Aurora — Developer The technical specialist. Aurora handles code generation, debugging, architecture decisions, and ticket management. She has direct access to development tooling and code repositories. ### Zara — Content Strategist The content and marketing specialist. Zara produces SEO-optimised content, manages editorial calendars, and builds content strategies aligned with business goals. ### Mina — Researcher The deep research analyst. Mina conducts multi-source research via MCP web search tools, synthesises findings, and delivers structured intelligence reports. ### Neda — Analyst The data and strategy analyst. Neda handles competitive analysis, data interpretation, trend identification, and strategic recommendations. She turns raw data into actionable insights. / 04 — THE WORKFLOW ## How It Works The flow is simple: send a message in Discord, Laila analyses it and routes tasks to the right specialist agents, they execute independently using MCP tools and memory, and results are compiled back to the user. All context, decisions, and outcomes are stored in persistent memory for future sessions. 1 ### Request User sends a message in Discord, anything from research competitor X to create a blog post about Y. 2 ### Orchestrate Laila analyses the request, retrieves relevant memories, and routes tasks to the best specialist agent(s). 3 ### Execute Specialist agents work independently, using MCP tools, accessing memories, and producing deliverables. 4 ### Synthesise Results are compiled, reviewed by the orchestrator, and delivered back to the user in Discord. / 05 — KEY FEATURES ## Key Features The system combines persistent memory, tool integration, local-first inference, and workflow orchestration into a cohesive platform. Here’s what makes it work. ### Persistent Memory * Every agent maintains independent long-term memory. They remember past conversations, decisions, user preferences, and project context across sessions. ### MCP Tool Integration * Agents connect to external tools via the Model Context Protocol. Web search, JIRA, Google Tasks, and custom integrations give agents real-world capabilities beyond text generation. ### Local or Cloud * Flexible deployment options — run local models on your own hardware or use a hosted subscription, depending on your needs. Maintain full control over your data with the ability to keep inference on-premise, or opt for managed infrastructure without being locked into a single approach. ### Workflow Orchestration * Complex multi-step workflows automated through your preferred tools—whether that’s n8n, Make, Zapier, or custom pipelines. Scheduled tasks, conditional logic, webhook triggers, and inter-agent communication are handled through flexible, interoperable workflow systems that fit into how your team already operates. ### Direct Agent Conversations * Beyond team orchestration, users can DM individual agents directly for focused, specialist interactions, like having a private chat with your developer or researcher. ### Works Where Your Team Works * No new tools to learn. Agents operate directly within your existing platforms—Slack, Teams, Discord, or wherever your team collaborates. Threaded conversations keep context organised, accessible, and easy to follow. ## Communication Modes The system supports three interaction patterns depending on what you need. Team channel orchestration for complex multi-agent projects, direct messages for focused specialist work, and scheduled tasks for recurring automation. ### Team Channel Post in the team channel and Laila orchestrates, routing tasks to the right agent and compiling results. Best for complex projects that need multiple specialists. ### Direct Message DM any agent for private one-to-one conversations. Perfect for focused work with a specific specialist without involving the full team. ### Scheduled Tasks n8n triggers workflows on schedules: daily reports, weekly summaries, automated monitoring. The team works while you sleep. / 07 — ARCHITECTURE ## Technical Architecture For the technically curious, here’s what’s under the hood. A 122B parameter mixture-of-experts model handles inference locally on an RTX Pro 6000 with 96GB VRAM. Mem0 plus pgvector provides persistent semantic memory. n8n orchestrates workflows and scheduled tasks. MCP servers expose tools for web search, project management, and custom integrations. ### Inference 122B MoE model via LocalAI on RTX Pro 6000 with 96GB VRAM. Cloud fallback via OpenRouter for overflow capacity. Full data sovereignty with on-premise processing. ### Orchestration n8n for workflow automation, task routing, webhook triggers, and scheduled operations. Visual workflow builder with conditional logic and error handling. ### Memory Mem0 persistent memory with pgvector for semantic search across agent conversation history. Each agent maintains independent memory stores for domain-specific context. / 08 — THE OUTCOME ## The Results We built this for ourselves first. It now runs continuously, supporting research, content production, development workflows, and project coordination. Agents collaborate within the platforms your team already uses—whether that’s Slack, Teams, Discord, or others—mirroring how human teams work, but with persistent context and the ability to operate around the clock. ## Want an AI Team? Whether you need AI agents for operations, customer service, content production, or research, we design and deploy bespoke multi-agent systems tailored to your workflows. [Build Your AI Team](/contact/)