Trends in Enterprise Agentic AI

Below is a general summary of major trends/activities we have noticed across macro, business, and technological dimensions that are currently shaping the “Enterprise Agentic AI” / “Autonomous AI Agents” space. These could offer some additional perspectives to both opportunities and risks we already know about the AI trend — and are useful lenses when thinking about AI business strategy, engineering/development, GTM, and product differentiation, and more so on if we are in an AI bubble or not.

1. Take on Macro Climate

1.1 Rapid market growth and hype risk

• The AI agent / agentic AI market is forecast to expand sharply — e.g. the global “AI agent” market is projected to grow from ~$5.1B in 2024 to ~$47.1B by 2030 (Extremely high CAGR ~ 44.8 %)

• Some market reports estimate alternate forecasts (e.g. the “agentic AI market” growing from $28B in 2024 to $127B in 2029).

• But Rapid growth comes hype risk: So, Gartner cautions that over 40% of agentic AI projects will be scrapped by 2027 due to unclear value, cost overruns, or poor execution.

• At the same time many enterprises (early adopters) remain in early stages: only ~15% of companies are actively exploring agentic AI now. Reaching 85% may remain a question mark for the rather short horizon all the Ai vendors have outlined in their earnings reports on when they can monetize their AI CapEx.

• One a +ve note, Deloitte projects that in 2025, ~25% of firms already using generative AI will pilot agentic AI, rising to ~50 % by 2027.

1.2 Platform and ecosystem race

• Large incumbents (e.g. GOOG, MSFT, AMZN, FB, CRM) are aggressively building native AI apps or bolting on agentic AI capabilities (e.g. Google launching Gemini Enterprise for business workflows).

• Cloud providers are viewing agentic AI as a strategic moat: e.g. AWS has formed an internal team focused on agentic AI development. And if you look at the menu of AI offerings that AWS, it tells how badly the company wants all stakeholders to be on AWS ecosystem. But it may find it very hard to compete in the enterprise AI landscape against GOOG and MSFT, given it does not have its own AI model yet where GOOG shines. In our mind, GOOG could be the one to look out for given it has a complete stack in addition to developer friendly environment. Distribution capability is also OK given they have the Android ecosystem.

• We are already noticing that the value gradually moves from AI models themselves to orchestration, integration, data, governance layers, general apps and vertical apps. As MSFT puts it, we are entering the “era of AI agents” where reasoning and memory architectures become critical infrastructure.

• Consultancy firms are also stepping in: e.g. PwC launched “Agent OS” to coordinate, certify, and connect agents across systems (aimed at enterprises). And you can see where Accenture is heading with its nonstop small scale/under the radar acquisitions that suits is consulting practice. Accenture CEO recently announced that the firm will lay off quite some consultants that are not aligned or may not play well with AI projects.

1.3 Pilots to scale

• The pattern seen in generative AI (many pilots, few scaled deployments) is repeated in agentic AI. Scaling AI agentic systems require foundational work: data pipelines, privacy, governance, tight monitoring, seamless integration, rapid experiments, and change management. Usual problems that were present in previous SW related innovation such as DevOps, DataOps, CloudOps, ITOps and now well, MLOps.

• Enterprises are demanding measurable ROI, risk control, auditability — AI agents can’t be black boxes. There is pressure to show real cost or revenue impact, not just “cool demos.” Once the euphoria of early adoption goes out, we will find out who is standing naked and holding the bags of massive AI investments. And the value of the investments can vaporize if a country like China can come up with more efficient AI models that does not need that many GPUs. It is only that is mostly drumming the beats the world needs GPU or there will be no AI. Our take - it will settle in 1-2 years as there will be rapid innovation on this front given it is the center of attraction or the honeypot now and many people must be already building their ladders to get to that honeypot

• Agent adoption may follow a gradual path: from augmentation (assistants) → supervised agents → semi-autonomous → fully autonomous in restricted domains. I think many current implementations are in the earlier levels (Level 1–2 autonomy).

1.4 Regulatory, security, and trust pressures

• As agentic systems become more powerful, governance, explainability, security, compliance, audit trails, and adversarial robustness become critical differentiators.

• Enterprises in regulated industries, mostly finance, healthcare, insurance, will demand additional constraints: model certification, human-in-loop for high-risk decisions, stringent data privacy and clear traceability.

• We see there is rising concern about autonomous decision liability (in both economics terms and criminal terms), decision errors, compliance lapses, unknown-unknowns (loopholes) and misuse. Risk mitigation and trust features may become immediate selling points for many new products for the short-term horizon.

1.5 Blurring of intelligence, automation, and orchestration

• The narrative is also shifting from “automate tasks” to “autonomous, continuous decision systems.” Agents are not just tools; they want to become active participants in processes.

• The convergence of hyperautomation (cliché), decision intelligence, and agent orchestration is accelerating (i.e., blending RPA + AI + workflow (IT) orchestration as is envisioned by, you guessed it, ServiceNow.

• More enterprise software (ERP, CRM, service platforms) is expected to embed agentic AI modules over time—embedding intelligence deeper rather than building standalone agent platforms.

2. Take on Business Sentiments

2.1 Expectations of ROI

• Stakeholders are increasingly skeptical of “AI for AI’s sake.” They demand clear cost reduction, revenue upside, or risk mitigation KPIs.

• Projects that are too ambitious e.g. full domain automation may fail in such early days; more conservative, incremental approaches (pilot → expand) are often safer. That is, instead of losing out on the entire pie, companies should start with small sale deployments that can show value of their products right away.

• Vendors will need to build strong success frameworks (use-case playbooks, vertical templates, reference metrics).

• Pricing and monetization models will evolve - usage-based pricing, outcome-based pricing, agent “seat + runtime + compute” bundles. From a SW centric pricing model to services-offered centric pricing. As Foundation Capital rightly put it, we are moving from Software-as-a-Service paradigm to the Enterprise Services-as-a-Software paradigm.

2.2 A hybrid workforce model

• The notion of a hybrid workforce with humans + agents will become more mainstream. Agents take over repetitive, rule-based, scalable tasks; humans focus on oversight, strategy, creative work. Interestingly this was a key vision outlined by several IT consulting firms that see such model as becoming a norm for their own future practices.

• Organizational roles may shift: e.g. “agent handler,” “agent manager,” “agent auditor,” “agent threat lead” may become formal titles – may feel like operating inside a large spy network.

• Although change management, training, cultural shift, and trust adoption are major non-technical barriers and look non-threatening, many internal teams may resist ceding control to agents and pose the biggest obstacle.

• There is already pressure for explainability and human-understandable reasoning to build trust.

2.3 Data maturity, lineage & integration

• Enterprises are increasingly realizing that data readiness, quality, integration, and metadata management are foundational for reliable agentic systems. Agents rely on live data pipelines and context.

• But many enterprises still have legacy systems, data silos, weak metadata, or poor instrumentation. The “last mile” work of integrating agents into existing systems is largely underestimated now.

• Organizations are frantically consolidating single sources of truth (business metadata, unified catalogs) to let AI agents reason intelligently across domains.

• Observability, logging, feedback loops, error detection, and rollback mechanisms will become crucial in production. It is all going to come down at once. Who is ready?

2.4 Domain specialization

• “Generalist” AI agents will have limitations; industry- or domain- or use case-specialized agents (finance, legal, compliance, supply chain, HR, cybersecurity) are already gaining traction as higher-value (RoI) initial use cases.

• Vertical templates, domain-specific data models, compliance modules, and domain-expert accelerators are becoming important differentiators.

• Agents often start good in narrower, constrained (less corner cases) domains where rules, metrics, and guardrails are well understood.

2.5 Ecosystem and partnerships

• To scale, vendors are building AI agent marketplaces or app stores (agents developed by third parties) and partner ecosystems (consulting firms, system integrators) to implement specialized agents.

• So, interoperability will matter - standards for agent communication protocols, memory/context exchange (e.g., Model Context Protocols or Agent Communication Protocols) are receiving increased attention.

• Alliances among cloud providers, enterprise software vendors (ERP/CRM/BI) and consultancies (Big 4, SIs) are becoming key distribution channels.

3. Take on Technological Advancements

3.1 Advances in reasoning, memory, and composition

• Beyond raw LLMs, I think the frontier could soon be at reasoning, planning, memory architecture, and agent chaining, agent context, multi composition (small multi-agent systems) to break down tasks into subagents.

• Improved long-term memory, retrieval, context window, and state management are enabling agents to maintain continuity over extended workflows.

• Compositional or modular agent design is also rising — tasks are decomposed into smaller agents working collaboratively rather than large monolithic agents. There could be some parallel to a Hadoop type sentiment here where a large agent breaks down an activity into smaller tasks and assigns the right agents and finally assembles the best output.

3.2 Multi-agent orchestration and coordination

• Agents coordinating with one another (delegation, negotiation, arbitration) is becoming essential for complex workflows.

• Orchestration frameworks that route tasks between specialist agents, manage dependencies, context, and synchronize state are advancing.

• Conflict resolution, agent-to-agent communication protocols, and emergent behavior control are under active research.

3.3 Grounding & multimodal perception

• AI agents are increasingly leveraging multimodal inputs (text, image, video, audio, structured data, sensor data) to act more richly in environments.

• “Grounding” — linking decisions and actions to real data, systems, APIs, and external context — is crucial. Without good timely grounding, agents are prone to hallucination or unsafe actions.

• Real-time data pipelines, event streaming, change capture, and situation awareness are also becoming standard enablers.

3.4 Model updates, adaptation, and fine-tuning

• AI agents need to adapt, retrain, and fine-tune over time based on feedback loops, failures, or changing business logic. As mentioned above, “grounding” is essential to avoid hallucination.

• Continuous model monitoring, feedback loops, retraining pipelines, and drift detection are essential AI infrastructure.

• Tools for “agent self-improvement” — meta-learning, agent spawning new subagents, or evolving logic — are nascent now but emerging.

3.5 Trust, safety, and verification tech

• Some companies are working on formal verification, model constraints, guardrails testing, and safety control (e.g. limiting action space, failsafe checks) are under development.

• Explainability (why an agent acted a certain way) and audit trails are key for enterprise adoption. There could be some related legislation coming this way.

• Adversarial robustness (resisting malicious inputs, external manipulations) will soon be part of baseline expectations.

3.6 Lightweight, efficient architecture and edge use deployments

• Running agents in production at scale requires model efficiency (cost of inference, latency, memory).

• Certain movements toward smaller, optimized AI models, quantization, distillation, or hybrid architectures (local + cloud) are ongoing.

• In some domains (IoT, edge, embedded systems, manufacturing), deploying agents close to data sources or offline may become vital.

3.7 Standardization, open protocols and agent tooling

• Frameworks like LangChain, AutoGen, CrewAI, SmolAgents, etc. are pushing standard agent architectures and reusable/repeatable building blocks.

• As noted earlier, standard protocols for agent communication, memory, model interchange, introspection (e.g. Model Context Protocols) are being discussed in the industry.

• Tooling for debugging, testing, simulating agents, and “sandbox environments” for agent validation are emerging as key infrastructure.

4. Implications & Strategic Considerations for AI Startups

• Positioning and moat: In this competitive & emerging field, a startup’s ability to combine autonomous agents + governance + real-time data integration + human-in-loop orchestration seems to be key to their defensibility.

• Vertical focus & domain specialization: Leading with domain templates (finance, compliance, ops) seems to accelerate adoption and credibility of products.

• Trust with governance as differentiation: Emphasizing auditability, fail-safe controls, explainability, risk management in product and marketing may be viable differentiators.

• Ecosystem + partnerships: Build alliances (cloud providers, enterprise systems, consultancies) and a marketplace for third-party agents will be needed for AI era business models.

• Incremental deployment strategy: Favoring conservative, bounded use cases early (e.g. agents in low-risk domains) and gradually expanding to more aggressive autonomy should be the GTM approach.

• Product roadmap priorities: investing in agent orchestration, memory, adaptation/self-improvement, agent-to-agent communication, debugging and observability tools should be in the roadmaps.

• Change management & adoption support: Providing frameworks, playbooks, training, governance scaffolding to help customers adopt agents safely will add positive views from potential customers.

• Leveraging data & integration advantages: Since agents live or die by context and data, forwarding data capabilities will be critical.

Below is a summary of our above take on various aspects on the AI landscape.

Time Horizon	Near-Term (2025 – 2026)	Mid-Term (2027 – 2028)	Long-Term (2029 – 2030)
Key Trends	Market formation and experimentation - Enterprises running pilot projects in agentic AI; hype vs ROI scrutiny. Trust & governance as top buyer concerns - Demand for auditability, human-in-loop, explainability. Data readiness gaps - many orgs still modernizing data pipelines and metadata. Cloud incumbents expand aggressively - Microsoft Copilot Agents, AWS Agent Framework, Google Gemini Enterprise. Rise of verticalized, domain-specific agents - Finance, HR, compliance etc.	Shift from pilot to production scale - Focus on cost savings, measurable KPIs. Standardization and interoperability - Emerging agent communication / memory protocols (MCP, MAPE). Hybrid workforce adoption - Formal roles for “agent manager,” “AI auditor” etc. Multi-agent orchestration maturity - Collaboration among specialist agents becomes standard. AI safety regulation wave - Audits, certifications, liability norms.	Autonomy Levels 3-5 - Self-learning, self-improving agents with minimal supervision. Continuous learning & meta-agents - Agents create/optimize other agents. Industry convergence - RPA + workflow + LLMs → “Unified Decision Engines.” Multimodal and real-time -Agents perceive via text + voice + video + structured data. Regulation and ethics - AI accountability, provenance, liability.
Opportunities	Startups should position themselves as the trusted AI vendor. Emphasize governance, transparency, human oversight. Use real-time data integration to differentiate specifically in regulated, data-intensive sectors. Develop vertical templates (e.g., compliance, finance ops) for fast-track pilots. Leverage Microsoft/Google like platform to piggyback on enterprise trust.	Expand into enterprise-wide orchestration layer (governing 100s of agents). Offer agent observability / audit dashboards as compliance differentiators. Partner with consulting and SI ecosystems for integration and governance frameworks. Adopt / help shape open agent protocols → platform neutrality.	Evolve into enterprise AI system managing “digital divisions.” Launch agent marketplace / ecosystem enabling 3rd-party extensions. Monetize governed autonomy at scale — agents that self-optimize yet stay compliant. Differentiate via trust, safety, interpretability IP (explainable agent cognition).
Risks	Execution risk - too many PoCs, few scaled deployments. Brand overshadowed by cloud giants’ platforms. Integration complexity across legacy systems. Customer hesitation toward autonomous workflows.	Need for capital and scale infrastructure. Compliance burden as various regulators codify agent governance. Standard wars — lock-in risk if startups do not align with dominant protocol. Operational complexity: managing fleets of evolving agents safely.	High R&D cost for self-learning capabilities. Societal and regulatory backlash against high autonomy. Cyber-security threats → agents as attack vectors. Consolidation: big clouds may absorb mid-tier platforms.

/ Service Ventures Team