Gartner expects 33% of enterprise software applications to include agentic AI by 2028. That projection is not a distant forecast – it is a description of deployments already underway at scaling companies. If your organization is still evaluating whether agentic AI is real, you are roughly two years behind the organizations setting the competitive baseline.
Agentic AI operates on a different principle than the AI tools most businesses use today. Where a chatbot or copilot responds to prompts, an agentic system plans sequences of actions, executes them using external tools, recovers from errors mid-workflow, and completes multi-step work without a human initiating each step. The defining characteristic is autonomy, not intelligence.
This article covers where agentic AI is headed through the end of 2027, which trends actually matter for business, what most forecasts get wrong, and what decisions you need to make now.
From Single Agents to Multi-Agent Systems
The first wave of agentic AI was about proving the concept: could an AI agent complete a 10-step workflow? The answer was yes, with caveats. Failure rates were high (often 20-50% on complex tasks), costs were unpredictable, and most deployments needed constant human supervision.
The second wave – the one building now – is about multi-agent orchestration. Instead of one agent trying to do everything, specialized agents handle distinct parts of a workflow and pass results to each other.
Think of it like hiring: a generalist can do many things poorly, or you hire specialists who each excel at one thing. Multi-agent architectures follow the same logic.
Jensen Huang, CEO of NVIDIA, put it directly at CES 2025: “Agentic AI is the next wave. AI agents don’t just answer questions – they take actions, complete tasks, and work alongside humans to get things done.”
What this means for business: Workflows previously considered too complex for AI automation – ones involving judgment calls, multiple data sources, and error recovery – are now viable. Customer onboarding, compliance checking, supplier negotiation prep, and research synthesis are moving from “not yet ready” to “deployable with the right architecture.” See our guide to agentic AI workflow automation for implementation patterns that work in production.
Small Language Models Are the Future of Agentic AI
The most consequential change in production agentic AI right now is happening at the model layer: small language models (SLMs) are replacing large frontier models as the backbone of real deployments.
Large frontier models (GPT-4-class, Claude 3-class) are expensive per token, have limited context windows for agent loops, and add latency. For one-off queries, cost is acceptable. For an agent that makes 50 decisions per workflow and runs 10,000 workflows per month, the math breaks down quickly.
SLMs change the equation. Models in the 3B-13B parameter range – including Microsoft’s Phi-3 (3.8B), Meta’s Llama 3 8B, and Mistral 7B – when fine-tuned on specific domains, perform agentic tasks at 10-50x lower cost per call with comparable accuracy on scoped tasks. According to Stanford’s AI Index 2024, the cost of AI model inference dropped approximately 90% between 2022 and 2024, a decline driven largely by smaller, more efficient architectures.
A specialized SLM for contract review does not need to write poetry. It needs to reliably flag missing clauses and extract key terms – a task where a fine-tuned 7B model can match frontier performance at a fraction of the cost.
The practical implication: Businesses that wait for perfect general-purpose AI will lose to competitors deploying good-enough specialized AI at scale. The capability gap between frontier and fine-tuned small models is closing fast for domain-specific work.
Memory and Context Are the Next Frontier
Current agentic AI systems have a core limitation: they largely start fresh with each interaction. Long-term memory – the ability to accumulate context about customers, processes, and history – is one of the most active areas of development.
Vector databases, episodic memory stores, and retrieval-augmented generation (RAG) systems are being combined to give agents access to institutional knowledge. An agent that helped onboard a customer three months ago can, in principle, recall the details of that onboarding when a support issue arises today.
McKinsey’s analysis of AI automation potential estimates that knowledge work functions – legal, financial analysis, customer operations – represent $2.6 to $4.4 trillion in annual productivity value. The unlock for most of that value is not processing speed. It is context: agents that know your customers, your processes, and your history well enough to act without being re-briefed every session.
Why this matters for business: Agents with memory become exponentially more useful over time. They move from “execute this task” tools to systems that accumulate organizational knowledge and use it proactively. Early movers building memory infrastructure now will have a compound advantage over latecomers.
The Reliability Gap Is Closing – But Slowly
Any honest assessment of agentic AI must address failure rates. In 2024, benchmarks showed agentic systems failing 20-50% of complex multi-step tasks. That number is improving, but the path from 70% success to 95% success requires careful task decomposition, fallback logic, and human-in-the-loop checkpoints at the right moments.
Dario Amodei, CEO of Anthropic, described the trajectory in 2024: “We may be approaching a moment where many instances of Claude work autonomously in a way that could potentially compress decades of scientific progress into just a few years.” That potential is real – but it assumes architectures built for reliability, not just capability.
The businesses succeeding with agentic AI are not deploying it on inherently risky tasks and hoping for the best. They are deploying on:
- High-volume, well-defined tasks where a 5% error rate is acceptable and recoverable
- Tasks with clear validation steps where the agent can verify its own output
- Workflows with human checkpoints at high-stakes decision points
For a breakdown of which agentic AI tools have the strongest reliability track record in production, we have covered the leading options with honest failure rate data.
What Most Trend Forecasts Get Wrong
Most agentic AI predictions focus on capability: what the models can do, how fast they are improving, which benchmarks they pass. That framing misses the actual constraint.
The bottleneck in most organizations is not model capability – it is organizational readiness. Specifically:
Data readiness. Agentic systems that need to query customer history, product catalogs, or internal documents require clean, accessible data. Most enterprise data is not clean or accessible. According to Forrester, 52% of AI project costs are spent on data preparation – before any model runs a single inference.
Process definition. Agentic AI requires clear goal and boundary specification. Processes where the definition of “done” shifts based on stakeholder mood, or where exception handling is handled by experienced humans using institutional knowledge, are not ready for agents today.
Governance and audit trails. Regulatory pressure is building around AI decision-making in high-stakes contexts. Organizations without audit trails for agent decisions face growing legal and reputational exposure – but most early deployments skipped governance architecture entirely.
McKinsey reports 70% of large-scale AI implementations underperform their initial targets. The cause is rarely the AI technology. It is misaligned expectations, insufficient process definition, and absent fallback logic.
What this means: When evaluating an agentic AI initiative, the right question is not “can AI do this task?” It is “is our data, process, and governance architecture ready to support an AI doing this task?” Most organizations that answer honestly find they need 3-6 months of infrastructure work before a deployment is viable.
At arsum, roughly 30-40% of initial project inquiries result in us recommending a narrower scope than the client originally proposed – or recommending that a specific process is not yet suitable for agentic automation. That honest scoping avoids the 70% failure rate. See our custom AI solutions guide for the framework we use to evaluate readiness.
What the Next 18 Months Look Like
Several developments will shape agentic AI adoption through the end of 2027. For each, here is what will accelerate and what will fail:
Model-to-model communication standardizes. Protocols like MCP and A2A are being formalized to enable agents to hand off tasks, share context, and verify outputs. This reduces custom glue code for multi-agent systems significantly. The agentic AI frameworks and framework selection guides covering these integrations are maturing rapidly. What will fail: organizations that build multi-agent systems now on proprietary protocols will face costly rewrites as standards solidify.
On-device and edge deployment grows. As SLMs become viable, agents that run locally – without sending data to external APIs – become practical for regulated industries: healthcare, legal, financial services, where data sovereignty is non-negotiable. What will fail: cloud-only architectures in regulated industries, where data governance requirements will block full deployment.
Agent marketplaces emerge. Pre-built specialist agents for common business tasks (invoice processing, lead scoring, compliance auditing) will lower the barrier to entry. Businesses will orchestrate pre-built agents rather than building everything from scratch. What will fail: vendors selling pre-built agents without customization pathways – the variance in enterprise data and process structure is too high for one-size-fits-all agents.
Cost structures shift. IDC projects global AI spending to grow at 28.5% CAGR through 2028. As inference costs continue falling, the ROI calculus for agentic deployments improves every quarter – meaning projects that were marginal in 2025 will be clearly profitable by 2027. What will fail: cost projections built on today’s frontier model pricing. SLM adoption will compress unit costs faster than most CFOs currently model.
Governance frameworks become mandatory. The EU AI Act, US executive guidance, and UK AI Safety Institute frameworks are converging on audit trail requirements for AI decision-making in high-stakes contexts. Organizations without structured logging and explainability built into their agent architectures will need costly retrofits.
What This Means for Your Business Now
Most business leaders are not asking whether to adopt agentic AI. The question is when and how.
The “wait and see” strategy has a cost. Competitors deploying now are accumulating two things: working automation (reducing their cost base) and institutional knowledge about what works (reducing their implementation risk). The gap compounds over time.
The “move fast and break things” strategy also has a cost. Poorly scoped agentic deployments, absent fallback logic, agents running on production data without validation – these generate incidents that slow down future adoption and damage trust with internal stakeholders.
The middle path: start with a narrow, high-volume, well-defined process. Prove ROI. Build confidence in the vendor relationship and the architecture. Then expand.
For companies evaluating where to start, the comparison of agentic AI vs generative AI is worth reading first – the distinction clarifies which processes are genuinely suited to agentic deployment versus which can be handled more cheaply with simpler generative tools.
Frequently Asked Questions
What is the future of agentic AI? Agentic AI is moving from single-task tools to multi-agent systems capable of autonomous end-to-end workflow execution. Key trends include smaller, cheaper specialized models (SLMs), persistent memory, standardized agent-to-agent communication protocols, and improved reliability through better architectures. Gartner expects 33% of enterprise software to include agentic AI by 2028.
Are small language models replacing large frontier models in agentic AI? For production, domain-specific deployments, yes – gradually. SLMs (Phi-3, Llama 3 8B, Mistral 7B) offer 10-50x cost reductions per inference call on scoped tasks. Frontier models retain advantages for complex, novel reasoning scenarios, but the performance gap is narrowing fast for well-defined business tasks. Stanford’s AI Index 2024 documents a 90% drop in inference costs between 2022 and 2024.
When should a business start deploying agentic AI? Now, with a narrow initial scope. Waiting for “mature” technology means ceding competitive ground to organizations already accumulating institutional knowledge about deployment. A well-scoped POC on a high-volume, low-risk process is the lowest-cost entry point. The goal is learning architecture and failure patterns on a contained process before expanding to mission-critical workflows.
What are the biggest risks of agentic AI adoption? The primary risks are: scope creep (agents making decisions outside intended boundaries), reliability failures on high-stakes tasks, and data governance gaps – organizations that have not audited what data agents can access and act on. A secondary risk is vendor lock-in: building on a single proprietary platform without an abstraction layer that allows migration.
How do multi-agent systems work? Multiple specialized agents, each with defined responsibilities, pass tasks and context between each other via orchestration frameworks. Each agent can use tools (databases, APIs, code execution) and escalate to humans or other agents when it encounters scenarios outside its confidence threshold. For a technical breakdown, see our comparison of agentic AI vs generative AI to understand how the architectures differ.
What industries will agentic AI disrupt first? Based on current deployment patterns, the highest near-term impact is in: financial services (loan processing, compliance monitoring, fraud detection), legal (contract review, due diligence, discovery), customer operations (Tier 1-2 support, onboarding, retention), and software development (code generation, testing, debugging). Common thread: high volume of structured tasks with clear validation criteria.
How much does it cost to deploy agentic AI? Costs vary widely by architecture. A contained POC with a pre-built framework (LangChain, CrewAI, AutoGen) on a single workflow typically runs $15,000-$40,000 for design, development, and initial deployment. Production systems with custom training, memory infrastructure, and multi-agent orchestration range from $80,000 to $300,000+. Well-scoped deployments in document processing or customer support typically reach payback in 6-18 months.
What is the difference between agentic AI and traditional automation? Traditional automation follows a fixed rule set: if X happens, do Y. It breaks when inputs fall outside the expected pattern. Agentic AI plans dynamically, uses tools to gather information, makes judgment calls within defined boundaries, and self-corrects when something unexpected occurs. The practical difference: traditional automation requires exhaustive rule specification upfront; agentic automation requires clear goal and boundary specification, with the agent handling execution.
The Companies That Move First Will Set the Standards
Agentic AI is not a future technology – it is in production at scaling companies today. The organizations defining best practices, building institutional capability, and refining their agent architectures now will not just automate faster. They will set the competitive benchmark that others will have to match under more difficult conditions.
The question is whether your organization is building toward that position or reacting to it.
If you are evaluating where to start or how to scope an agentic AI initiative, arsum works with companies to design and build production-ready agentic systems – starting with a proof-of-concept scoped to deliver measurable ROI before any large-scale commitment.