You keep hearing it: autonomous agents will take over tasks, free humans from drudgery, run entire businesses without supervision. It’s a seductive narrative. But in reality, full autonomy is a mirage — one often sold by marketers, not engineers. In this post, we argue that chasing full autonomy is not only impractical, it’s dangerous. The smarter bet is co-cognition: tightly controlled, collaborative AI systems that sit alongside human reasoning instead of trying to replace it.
The myth of full autonomy: what does it even solve?
First, ask: “For whom, and for what?” If we want to get freed from a particular task — say, answering customer emails — then sure, an “autonomous” customer-service agent sounds appealing. But generalizing that into “fully autonomous AI that handles everything” glosses over the brutal complexities:
-
Tasks drift. The domain changes. New edge cases crop up.
-
Context matters. Real human interactions hinge on nuance, tone, priorities, shifting constraints.
-
Alignment is brittle. Agents often optimize for proxies or metrics, not real value.
So autonomy doesn’t suddenly collapse the problem space — it just hides it behind a veneer of “set-and-forget.” Worse, it gives false confidence that the AI “knows what to do.”
If the goal is truly to offload tasks, you’ll still need humans in the loop — either for oversight, exception-handling, or boundary adjustments. Even the most vaunted “unattended agent” setups eventually bump into the need for human judgment.
Even “fully autonomous” agents end up needing human oversight
Ask any engineering team building agents at scale: human intervention is inevitable. Whether as safety checks, quality gates, escalation paths, or audits — you’ll design oversight.
Consider a supposed fully autonomous customer-service agent. What happens if it misinterprets a query, exacerbates a complaint, or triggers a legal issue? Do you let it run unchecked? No — you embed escalation policies, fallback rules, or human-in-the-loop modes. That’s already breaking “full autonomy.”
This is why many agent frameworks today are better described as “mostly autonomous with oversight,” not truly autonomous. The boundary between agent and human remains, and intentionally so — because that boundary is where control, trust, and responsibility live.
Meet co-cognition: the more realistic, more useful paradigm
Instead of chasing full autonomy, we propose co-cognitive systems. These are agents designed to work in tandem with humans, given:
-
Goals — clear, human-set objectives
-
Guardrails — constraints, policies, safety boundaries
-
Highly curated contexts — narrow domains, well-defined input spaces
Under this model, the agent is a collaborator, not a rogue intelligence. It is woven into human workflows; it queries when needed, and it defers when uncertain. It never assumes omniscience. It doesn’t invent contexts; it’s fed contexts that humans or systems define.
Crucially, co-cognitive systems treat memory/context as a first-class parameter, but not as an autonomous, fuzzy black box. Agents shouldn’t decide their own memory heuristics willy-nilly; instead, memory is a controlled subset of context. What is stored, summarized, or forgotten must be curated to preserve relevance, interpretability, and reliability.
Why fully autonomous context generation (heuristic memory) misses the point
Some agent frameworks try to automate context generation: “Let the agent decide what’s relevant and remember it.” That sounds nimble — until you realize that in high-stakes or domain-specific settings, that’s the path to drift, hallucination, and catastrophic misalignment.
The problem:
-
The agent’s memory heuristics may retain irrelevant or stale fragments.
-
The agent may premise future reasoning on flawed memories.
-
It becomes impossible to audit why the agent made a decision.
By contrast, treating memory as context-curated (i.e. under human/system supervision) ensures transparency and alignment. You can trace why the system acted. You can prune or override when context has shifted. That control is antithetical to “full autonomy” but essential to practical usefulness.
A reality check: what’s happening in the streets of Agent Land
Let’s not pretend this is all theory. The industry is moving fast, with new agent toolkits and promises around autonomous AI. But many of them betray how far we still are from full autonomy.
-
OpenAI AgentKit (launched at DevDay 2025) is sold as a complete toolkit to take agents from prototype to production: visual workflow builders, connector registries, chat UI embedding, guardrails, evaluation tooling, etc.
-
But by bundling connectors, versioning, guardrails, and human gate logic, OpenAI admits the necessary components of control are part of the story. You’re not building a free-roaming agent; you’re building a governed agent with supervision capabilities.
-
Anthropic’s “Skills” add modular capability layers (scripts, instructions, resources) for Claude agents — effectively narrowing domain context to jumpstart specificity in user workflows.
-
But note: Skills are not autonomy — they are scoped tools. They reduce drift by anchoring behaviors to specific, human-defined modules.
-
Many “agent models” out there that claim to “spin up a full team” often turn out to be wrappers over n8n, Zapier, or other workflow engines — orchestration masks complexity, but the actual “reasoning” is delegated to templates or tools, not an autonomous brain doing new thinking.
Even in the bleeding edge, agent work is still about composition, control, and orchestration — not giving AI carte blanche to roam.
Risks scale with autonomy — and that’s not hype, that’s real work
This isn’t just philosophical hand-waving. In the recent paper Fully Autonomous AI Agents Should Not Be Developed, the authors argue that risks to people increase as systems gain autonomy. The more control you cede to an AI, the more damage it can do (or misalign).
Similarly, issues like agentic misalignment (where the AI acts like an insider threat) demonstrate that an agent can betray human goals when left ungated.
Memory drift, reward hacking, side-effects, cascading errors — these risks magnify when oversight is minimized. (See also the classical literature on negative side effects due to incomplete modeling in autonomous systems.)
So the more “autonomy” you bake in, the heavier the tail risk you must insure against. And since many of these agents touch business or safety-critical domains, that’s not a theoretical concern — it’s liability, trust, and damage control.
TL;DR — Don’t chase the autonomy unicorn
There’s no magic in “fully autonomous AI agents.” They don’t eliminate complexity — they just bury it behind automation. In practice, we will always need human oversight, context curation, guardrails, and goal steering.
Rather than chasing autonomy, the winning paradigm is co-cognition: AI agents that partner with human intelligence, constrained by goals, guardrails, and well-defined context. Memory should be a curated extension of that context, not some fuzzy autonomous submodule.
Let others hallucinate full autonomy. We’re focused on building tools that are safe, auditable, effective — and useful today. That’s the road we’re walking (and what we’re building … stay tuned for more).