Agentic AI: What Mid-Market Leaders Need to Know
Agentic AI - systems that can plan, decide, and act autonomously - is the next major shift. 40% of enterprise apps will feature AI agents by 2026. Here's what it means for your business.
6 min read · By Jamie Oarton · Last updated March 2026
Agentic AI refers to AI systems that can autonomously plan, make decisions, and take actions to achieve goals - rather than simply responding to prompts. Where traditional AI answers questions, agentic AI completes tasks: researching, comparing options, making decisions, and executing across multiple systems without human intervention at each step.
According to Gartner's 2025 predictions, 40% of enterprise applications will feature task-specific AI agents by 2026, up from less than 5% in 2025 (Gartner, 2025). The global AI agents market has already reached approximately $7.6–7.8 billion in 2025 and is projected to exceed $10.9 billion in 2026.
This is the fastest-moving area of AI - and the one most likely to catch mid-market companies off guard.
What Makes Agentic AI Different
| Traditional AI | Generative AI | Agentic AI | |
|---|---|---|---|
| What it does | Analyses data, makes predictions | Generates content from prompts | Plans and executes multi-step tasks |
| Human involvement | Reviews outputs | Writes prompts, reviews outputs | Sets goals, reviews outcomes |
| Autonomy | None - follows rules | Low - responds to instructions | High - decides how to achieve goals |
| Example | Spam filter, recommendation engine | ChatGPT writing an email | AI agent that researches vendors, compares pricing, drafts a recommendation, and schedules a meeting |
| Risk profile | Low (predictable) | Medium (output quality varies) | High (autonomous decisions, data access) |
Why It Matters for Mid-Market Companies
The opportunity
Agentic AI can automate entire workflows, not just individual tasks. For mid-market companies where teams are stretched thin, this is significant:
- Back-office automation: Invoice processing, expense reconciliation, compliance checking - done autonomously
- Customer operations: Agents that handle customer enquiries end-to-end, escalating only when necessary
- Sales support: Research prospects, prepare briefings, draft proposals, schedule follow-ups
- Data operations: Monitor data quality, flag anomalies, generate reports on schedule
The risk
Agentic AI systems have access to data, make decisions, and take actions. That combination creates risks that traditional and generative AI don't:
- According to Akto's State of Agentic AI Security 2025, 79% of enterprises have blindspots where agents invoke tools, touch data, or trigger actions that security teams cannot fully observe (Akto, 2025)
- 80% of organisations have encountered risky behaviours from AI agents - including improper data exposure and unauthorised access (Akto, 2025)
- Only 17% continuously monitor agent-to-agent interactions, and only 38% monitor AI traffic end-to-end (Akto, 2025)
According to Gartner, over 40% of agentic AI projects will be cancelled by end of 2027 due to escalating costs, unclear value, or inadequate risk controls (Gartner, 2025). The pattern mirrors what happened with generative AI pilots - enthusiasm without strategy leads to waste.
The Governance Challenge
Agentic AI amplifies every governance gap in your organisation. If your AI governance is weak for generative AI, it will be dangerous for agentic AI.
Consider: a generative AI tool like ChatGPT can leak data if an employee pastes sensitive information into it. An agentic AI system can autonomously access your data, make decisions based on it, and take actions across your systems - all without a human in the loop.
This requires governance that goes beyond the Four-Step AI Governance Model for shadow AI:
| Governance layer | Generative AI | Agentic AI |
|---|---|---|
| Data access | User pastes data in | Agent accesses data autonomously |
| Decision authority | Human decides | Agent decides (within boundaries) |
| Action scope | Generates content | Executes across systems |
| Monitoring | Usage logs | Full audit trail of decisions and actions |
| Rollback | Edit or delete output | Undo multi-system actions |
| Accountability | User is accountable | Who is accountable for agent decisions? |
What Mid-Market Companies Should Do Now
1. Don't panic, but don't ignore it
Agentic AI is real and moving fast, but it's not ready for unsupervised deployment in most business contexts. According to Gartner, 15% of day-to-day work decisions will be made autonomously by agentic AI by 2028, up from 0% in 2024 (Gartner, 2025). You have time to prepare - but not to wait.
2. Get your AI governance right first
If you haven't addressed shadow AI and basic AI governance, agentic AI will magnify those problems. Build the foundation (the Four-Step AI Governance Model and the 90-Day Governance Framework) before deploying autonomous systems.
3. Start with constrained agents
The safest entry point is AI agents with tightly defined scope - a specific task, limited data access, human approval for decisions above a threshold. As trust builds, scope can expand.
4. Demand observability
Any agentic AI system you deploy must have full audit trails: what data it accessed, what decisions it made, what actions it took, and why. If you can't observe it, you can't govern it.
5. Include agentic AI in your AI strategy
If you're building or updating your AI strategy using the AI Strategy Compass, agentic AI should be explicitly addressed - not as a current initiative, but as a capability you're preparing for. This means governance frameworks that can extend to autonomous systems, data infrastructure that supports agent access, and leadership understanding of what's coming.
Frequently Asked Questions
Is agentic AI the same as AI automation?
Not quite. Traditional AI automation follows predefined rules (if X then Y). Agentic AI can reason about goals, plan multi-step approaches, adapt to unexpected situations, and make decisions. The distinction matters because automation is predictable; agentic AI is not always predictable.
Should mid-market companies be deploying agentic AI now?
Most should be preparing rather than deploying. Get your AI strategy, governance, and data readiness in order first. Then identify specific, constrained use cases where an AI agent could add value with manageable risk. Full-scale agentic deployment is premature for most mid-market companies in 2026.
What's the biggest risk of agentic AI for businesses?
Accountability. When an AI agent makes a bad decision - sends the wrong data to a client, approves an expense it shouldn't, or takes an action that violates compliance - who is responsible? Current governance frameworks don't adequately address this. Building that accountability framework before deployment is essential.
How does agentic AI relate to what my employees are already doing with ChatGPT?
It's the next step. Right now, employees use AI as a tool - they prompt it, review the output, and decide what to do. Agentic AI removes the human from parts of that loop. The governance challenge scales accordingly: shadow AI is risky because employees share data with AI tools; agentic AI is riskier because the AI acts on that data autonomously.
Will agentic AI replace jobs?
It will change jobs more than replace them. The most likely impact for mid-market companies is that existing roles become more strategic - less time on routine tasks, more time on judgment, relationships, and decisions that require human context. Companies that prepare their workforce for this shift will gain advantage over those that don't.
Jamie Oarton
AI strategy advisor and fractional Chief AI Officer through Bramforth AI. Helping UK mid-market businesses build AI strategies that connect to how they make money.