Posts Tagged ‘Agentic AI’
Agentic AI in Private Markets: Transforming Origination, Due Diligence and Value Creation
Private markets have long been characterised by information asymmetry, fragmented data, complex workflows, and highly skilled professionals making decisions based on incomplete information. Whether in private equity, private credit, infrastructure, real estate, or venture capital, investment teams spend enormous amounts of time gathering information, analysing documents, producing reports, and coordinating activities across multiple stakeholders. While…
Read MoreAgent to Agent (A2A) vs. Model Context Protocol (MCP)
Agent‑to‑Agent (A2A) vs. Model Context Protocol (MCP) — what’s the difference, and how to use them in asset & wealth management. Summary MCP standardises how an AI agent connects to tools and data (filesystems, databases, SaaS) through a client‑server protocol, solving the “N×M” integration problem and enabling secure, governed access to context. Think vertical integration:…
Read MoreFrom Pilots to Platforms with Gen-AI for investment research, client communications and investment performance reporting commentary.
Summary; Pilots to Platforms with Gen AI and Agentic AI In asset and wealth management, AI has lingered too long in proofs‑of‑concept—interesting demos that rarely touched investment research, quarterly investment snapshots, factsheets, RFP responses, or client reporting. That phase is ending. With governance‑first platforms, multi‑agent orchestration, and human‑in‑the‑loop controls, ‘agentic AI’ is crossing the chasm…
Read MoreLTM vs LLM: The Essential Guide for Financial Services Leaders
Here’s a clear explanation, grounded for financials services practitioners, of what an LTM (Large Tabular Model) is and how it differs from the development path of LLMs for tables and Gen‑AI so far. What is an LTM? An LTM (Large Tabular Model) is a type of AI foundation model designed specifically for structured data,…
Read MoreAgentic AI in Financial Services: From Pilots to Platforms
Executive takeaways for asset & wealth management teams; Investment marketing communication, research and client communication Agentic AI is moving from pilots to platforms. Microsoft, Google, Anthropic and Cohere have introduced enterprise‑grade orchestration, governance and multi‑agent capabilities designed for regulated environments. These are now viable for client reporting, performance attribution, and commentary workflows—provided you pair…
Read MoreAgentic Automation in Financial Services: Why Orchestration Is the Real Challenge
Agentic AI—systems where autonomous agents handle tasks—has generated huge excitement. But the biggest obstacle isn’t what most people expect. It’s not about reasoning power, speed, or access to tools. The real bottleneck is orchestration: how we coordinate multiple agents to work together effectively. What’s Wrong with Current Approaches? Most teams use simple strategies like: Serial…
Read MoreBreak Free: Why Your AI Strategy Needs to be LLM-Agnostic
Microsoft has just announced their Copilot Wave 2 spring release, touting new capabilities like AI-powered search, Copilot Notebooks, and an Agent Store for accessing specialised reasoning agents. Whilst these developments certainly represent progress, they highlight a critical concern for forward-thinking organisations: the risk of vendor lock-in. When you build your knowledge bases, workflows, and custom…
Read MoreAgentic AI: Transforming Financial Services Beyond Generic Solutions
Technology evolves at an accelerating rate, and we are currently witnessing a significant transformation. Agentic AI, artificial intelligence systems that independently pursue goals, make decisions, and take actions with minimal human intervention, is set to fundamentally change how financial services operate. As someone deeply embedded in the financial services technology landscape, I’ve been following the…
Read MoreWhat the Future Holds: From LLMs to AI Agents
In 2024, we saw a dramatic shift in interest towards AI “agents” and, more generally, systems that allow domain expert humans and AI agents to work collaboratively towards a goal or automate the handling of incoming tasks. Despite having existed as a term in AI for decades (specifically reinforcement learning), “agent” has become a loosely…
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