Published on 8/14/2026
AI agents have come a long way. They evolved from calling tools, to coordinating teams of sub-agents, and now to handling ever-larger contexts. But for most agents, doing something for the 100th time is no different from the first. Unlike humans, they don't grow more capable through experience. Today, what holds agents back in real work is no longer model capability, tools, memory, or context - increasingly, it's real-world experiences, and the ability to learn from them.
Most attempts to solve this problem don't touch the core issue:
Application wrappers / agent harnesses. They can connect tools and organize workflows, but the model itself stays static. Their capability ceiling remains that of the model underneath.
RAG / external knowledge bases. They can pull facts from documents, conversations, and databases. But what they can't retrieve is the expert's way of thinking - how decisions get made, why revisions happen, what "good" actually looks like. That never makes it into the model. So every time the AI picks up a task, it starts from the raw material all over again.
Fine-tuning. Periodic retraining is expensive, slow, and always a step behind the business.
Self-reflective learning. Without expert anchoring or a reliable way to evaluate itself, a model just circles at its own level - or worse, drifts off-course.
The problem isn't that tools, knowledge bases, or fine-tuning are not useful. It's that knowledge and experience stay outside the model. These approaches give AI more information, but they do not make the agent itself more capable. What lives inside the model can continue to compound and evolve the agent.
At the same time, this kind of experience data is exceptionally scarce, because it's produced only through real work. It's usually private, dynamic, and surfaces during the process - sometimes existing in nothing but one person's head.
What actually grows an agent from a junior assistant into a seasoned partner is work experience - the kind that carries judgment and feedback. And that kind of experience simply isn't on the internet. This is exactly where general-purpose LLMs hit their ceiling.
Our approach: full stack, model + application #
At Alloomi, we take a full-stack approach, combining the application layer and the model layer.
Agents work inside real workflows, capturing professional data that doesn't exist in public domains; and what they learn from that work is built back into the model through post-training. In practice, this happens across four layers:
Holistic context - so the model can see the whole picture
Bring people, conversations, documents, relationships, timelines, decisions, outcomes, and feedback into a unified context, then continuously track the complete trajectory of the work.
Traditional RAG assumes that facts live in static documents. But in real work, facts are revised, overturned, and may carry different meanings for different customers.
Retrieval answers "what happened." Holistic context answers "how what happened became what is."
A self-evolving memory model - learning while working
Context, expert judgment, revision histories, delivery outcomes, and customer feedback from real work are filtered, replayed, and used for post-training. The resulting experience is written into the model's own weights, rather than living in an external database.
Expert anchoring - keeping capability moving up
During learning, the model is anchored to expert demonstrations and the standards of the best deliverables. That way, it does not circle at its own level, nor let errors compound.
Controlled evolution - making evolution safe
Self-evolution is only useful if it's safe. Quality gates, continuous monitoring, and automatic rollback keep every model change verifiable, auditable, and reversible - drift is caught and undone before it matters.
And that completes the flywheel: every task finished is data earned; every judgment captured compounds into the next delivery. The work makes the agent better, and a better agent makes better work.
The technical foundation: nine benchmarks for memory, learning, and delivery #
The four layers describe how Alloomi works. We test Alloomi where it counts: does it understand the whole picture, does it keep learning, and does it deliver professional work. Nine benchmarks trace the entire chain from long-term memory to real-world delivery.
Holistic understanding - it remembers
Real work spans many conversations, many tasks, and long stretches of time. To participate productively, an agent has to remember what matters, understand how people, events, and time connect, and hold onto the whole picture as its history keeps growing. Three benchmarks show Alloomi's context capabilities:
On BEAM, Alloomi achieves global task accuracy of 72.8% at 128K, 75.7% at 500K, 76.5% at 1M, and 67.0% at 10M. Even at the 10M scale, it maintains 67.0%, above Hindsight's 64.1%, demonstrating that it can preserve relatively stable global task performance across extremely long histories.
On LongMemEval-S, Alloomi reaches 97.6%, compared with 94.4% for Memo-V3. This validates long-term memory across information extraction, knowledge updates, and multi-session reasoning.
On LoCoMo-V2, Alloomi scores 97.4%, compared with 92.5% for Memo-V3, demonstrating its ability to handle cross-session question answering, temporal relationships, and multi-hop reasoning - not just retrieving isolated facts.
Continual evolution - it learns
Memory is only the beginning. Beyond that, a long-horizon agent faces three harder tests: learning new rules from complex context, transferring experience across tasks, and keeping hard-won capabilities intact as the environment changes. We tested three evolution-related benchmarks here:
On CL-Bench, Alloomi improves from the GPT-5.6 Sol baseline of 21.5% to 47.6%, a gain of 26.1 percentage points. This demonstrates its ability to learn new rules from complex context and apply them in execution.
On CL-Bench-Life, performance rises from the GPT-5.5 (high) baseline of 22.2% to 32.1%, an improvement of 9.9 percentage points. The result shows that the model can accumulate experience over long-horizon tasks and transfer it to later stages.
On Con.L Bench, performance increases from the Claude Sonnet 4.6 baseline of 22.3% to 32.6%, a gain of 10.3 percentage points. This evaluates whether the model can continue learning across tasks while reducing the loss of previously acquired capabilities.
Together, these results show that Alloomi doesn't just adapt once - it compounds experience over time and carries it across tasks.
Professional delivery - it ships
Remembering and learning ultimately matter only if they improve real work. This group moves beyond memory or learning in isolation and tests whether Alloomi can deliver results across high-value professional tasks, complex real-world workflows, and continual software engineering.
On GDPval-AA Normalized, Alloomi reaches 74.2% across high-value tasks spanning 44 occupations, improving on Claude Opus 5's 67.9% by 6.3 percentage points. This indicates that long-term context and continual learning can translate into stronger performance on complex professional work.
On JobBench, Alloomi scores 57.5%, compared with 54.7% for Muse Spark 1.1 - an improvement of 2.8 percentage points. This validates its ability to produce better results on real occupational tasks and professional deliverables.
On SWE-Bench-CL, Alloomi reaches 80.6%, compared with 73.3% for OpenCode + Kimi K3 + FAISS, a gain of 7.3 percentage points. The result shows that the system can not only resolve real GitHub issues, but also retain and build on its capabilities across a sequence of software-engineering tasks.
Together, these benchmarks show that improvements in memory and learning can ultimately translate into measurable gains in real-world work delivery.
Detailed experimental settings, evaluation methods, and reporting conventions for all nine results are available in two public technical reports:
We have published these details so readers can verify the results, and we welcome discussion and corrections from the community.
From technology to products #
Today we're doing two things: building Alloomi AI, digital employees for professional services, and open-sourcing OpenContext, the context layer that powers it.
OpenContext is our context runtime for AI agents, now open-sourced. It is the working implementation of the holistic context layer. It embeds into agentic applications as a context harness, providing temporal context, memory and retrieval, context correction, multi-platform connectivity, and proactive scheduling. The project is young - try it, break it, contribute, and help us map what context harnesses can become.
Alloomi AI is our commercial product for individuals and teams in professional services: an OKR-driven, outcome-focused system of self-evolving digital employees. It understands long-term business context, breaks down and advances work on its own, and converts expert judgment, execution history, and outcome feedback from real operations into capabilities unique to each customer. We've already validated this through early co-design in some of the most specialized fields - legal, insurance, and financial advisory - and we're continuing to work closely with early users on their core needs while expanding into more specialized domains.
We believe the next phase of AI agents won't be decided by how intelligent a model is, but by what it learns from every piece of real work it does in the real world.
Every frontier model is brilliant out of the box, and identically so for everyone who uses it. What no one else can copy is what an agent accumulates inside your business: the judgment calls it absorbed, the revisions it learned from, the standards it internalized delivery after delivery.
Intelligence is where every agent begins. Experience is where they diverge. That's the future we are building for.