We Wanted Intelligence, We Got Automation

Before we begin, a preface: things are moving fast, and the info here may change what reality looks like tomorrow. That said, the core concepts will still apply.

The rapid proliferation of AI agents in 2025 illustrates a mismatch between expectations and reality. Enterprises imagined a world of adaptive, self-improving digital colleagues (and they were right to imagine that), agents that could learn, collaborate, and autonomously optimize workflows. Instead, most deployments rely on static, transactional products built on event-based, if-this-then-that logic, falling far short of the original vision. Lots of buzzwords, lots of 'DM AI for this Workflow' posts.

This briefing touches on the common trends in the AI agent space, where we may have missed the mark, and how some of these are being addressed.

Workflow Deployment: Ambition vs. Outcome

Organizations aimed to orchestrate complex processes end-to-end using teams of specialized AI agents. These agents were supposed to boost efficiency, anticipate bottlenecks, and seamlessly route work between humans and machines. The reality has been a washed-down version of that.

• Most agents handle only isolated tasks within siloed systems, rarely integrating deeply with real business logic or adapting to exceptions and changing conditions.
• Highly-touted deployments often stall at pilot stages, facing technical complexity, data silos, and loss of trust from users expecting more adaptability.
• A saturated marketplace promotes hundreds of transactional, event-driven solutions that automate discrete events but fail in the nuanced, complex workflows where real business value is measured.

The Saturation of Event-Based Products

The industry's overreliance on event-driven architectures, where agents react to specific triggers like new orders or document submissions, creates key challenges:

• Superficial automation: agents automate simple, predictable outcomes but lack contextual understanding and can't adapt when the environment changes or exceptions occur.
• Operational chaos: agent sprawl leads to fragmentation, security risks, and management headaches as countless bots proliferate without oversight or governance.
• High maintenance: distributed event workflows require constant monitoring and troubleshooting, especially as APIs shift or data formats change.
• Data consistency problems: asynchronous, transactional flows often result in delays, sequencing errors, and out-of-sync records, particularly in multi-party or regulated environments.

Why it feels saturated and ineffective

• Commodity core: everyone ships on the same two or three frontier models; the deltas are UI, prompts, and a Zapier or n8n-like graph.
• Thin moats: agent frameworks are replicable; no proprietary data, no advantage.
• Demo bias: happy-path videos hide edge-case handling, governance, and recovery logic, the real work.
• Misaligned buyer value: enterprises buy risk reduction and guarantees; most agents sell novelty and speed.
• Ops drag: monitoring, evals, prompt versioning, and data pipelines quietly eat margin.

Human-Machine Collaboration: Unexpected Consequences

Instead of machines learning to adapt and collaborate like humans, the opposite has often occurred: humans must adjust their behavior to fit rigid, protocol-driven logic dictated by AI agents. A human-as-machine phenomenon arises, where team members follow mechanical rules to accommodate system limitations, stunting creativity and flexibility. This warps the original intent, to have systems that enhance human judgment and improve with use.

Moving Beyond Saturation

The next phase of AI agent innovation is shifting away from static, reactive automations and toward adaptive, context-aware, and collaborative systems:

• Retrieval-enhanced architectures: agents that leverage external data sources for recency and accuracy, reducing hallucinations and improving transparency and trust.
• Collaborative and adaptive agents: agents that work alongside human teams, support decision-making, continually learn from feedback, and adapt their logic to emerging needs.
• Governance and compliance: agentic governance is now seen as essential, akin to cybersecurity, with guardrails, audits, and oversight becoming standards.
• MCPs: rather than every use case spawning another unmaintainable agent, MCPs turn agents into Lego blocks, small validated processes composed dynamically and updated independently.
• RAG: agents fail when hallucinating on stale data. RAG grounds each reasoning step in authoritative context.
• AgentOps: an emerging operational discipline for managing, governing, and optimizing intelligent agents across hybrid, multi-system environments.

What to build if you're building now

• Pick a painful, auditable task with a ground truth and a review surface (e.g., VC diligence extraction, scoring, memo draft with citations).
• Instrument first: traces, costs, latencies, failure reasons; label everything from day one.
• Constrain the model: tool calling via JSON schemas, deterministic planners for routing, small models for classification.
• Design review loops: inline diffs, one-click revert, why explanations, and uncertainty flags.
• Own a dataset: proprietary corpora plus feedback loops (accepted vs. edited actions) equal a compounding moat.
• Ship guarantees: SLAs for accuracy thresholds, bounded spend, and bounded steps, displayed in-product.