Cognizant AI Lab

TerraLingua hasn’t stopped evolving since we published our first look at its emergent behaviors. As new autonomous agents enter the world, they inherit an environment already shaped by those that came before them. They encounter artifacts, beliefs, social norms, conflicts, and information that can be preserved, challenged, reinterpreted, or passed on to future generations. That makes the evolution of the world itself increasingly interesting to study. Which behaviors persist? Which disappear? How does information change as it moves across generations? And what new dynamics emerge as agents continuously influence one another? Our latest blog captures five early examples, but they represent only a fraction of what has unfolded inside TerraLingua. Read the stories in the blog: https://cgnz.at/6048ayJUe Explore the full list of discoveries in r/TerraLingua: https://cgnz.at/6041ayJU9 Join the world today: https://cgnz.at/6042ayJUi #TerraLingua #AIAgents #AgentSociety

For most of the last decade, the basic unit of computation in AI has been a single model answering a single request. That is changing fast. Agents are beginning to run continuously, discover one another's capabilities over open networks, coordinate without a central operator, and adapt as the network around them keeps changing. The science for that regime does not really exist yet. The Agentic Web: Decentralized, Continually-Adapting Agent Ecosystems is a new workshop at NeurIPS 2026 in Atlanta built around exactly that gap, organized by Cognizant AI Lab's Risto Miikkulainen alongside Pradyumna Chari from Project NANDA: The Internet of AI Agents, Eric Horvitz from Microsoft, and Zixuan Ke from Salesforce. The call for papers is open now. The workshop invites work on questions including: • How agent capabilities can be represented and discovered at web scale • How learning, credit assignment, and self-improvement happen across networks that never stop changing • How large populations of agents coordinate, stay trustworthy, and remain safe under strategic pressure Short papers (4 pages) and long papers (9 pages) in NeurIPS format, submitted through OpenReview. The deadline is September 5. Read for full details: https://lnkd.in/e3TiE8tx #NeurIPS2026 #AgenticAI #MultiAgentSystems #AIResearch #MachineLearning

Every enterprise running agentic AI right now has some version of the same story. The first proofs of concept worked, the demos landed, budget followed, and then the deployment that was supposed to reach production quietly did not. The instinct is to blame the model and wait for the next one, and that instinct is almost always wrong. The Agentic Enterprise, the new O'Reilly book from Babak Hodjat, Chief AI Officer at Cognizant, and Antoine Blondeau, Founder and Managing Partner at Alpha Intelligence Capital, is about the decisions that come before the model. Hodjat and Blondeau have been building multi-agent systems together for more than 25 years, starting with the natural language agents at Dejima that eventually found their way into Siri, and they wrote the book they would have wanted back then. Their argument is that agentic systems fail on architecture rather than capability. Agents proliferate, stacks diverge, dependencies accumulate on a single provider, and what looked elegant in a demo becomes something nobody wants to put in front of a regulator. No model release fixes that, and neither does a governance policy written after the architecture is set. Across four parts, the book traces where the returns are real, drawing on organizations at scale rather than pilots, including Allianz Partners automating as much as 90% of eligible claims. It covers the technical foundations of grounding, coordination, memory, and planning, names the failure modes that surface in nearly every growing agent network, and makes the case that trust and scale are the same problem. Yann LeCun called the book "an important contribution to the global conversation on how enterprises can harness agentic AI to drive long-term competitiveness and innovation." Read more: https://cgnz.at/6047aHjav Order the book today: https://cgnz.at/6049aHjax #AIAgents #EnterpriseAI #AgenticAI #Book

An AI agent just broke a math record that had gone unchallenged since 2011. Our Principal Research Scientist, Jason Liang, gave an AI coding agent built on Anthropic's Claude a single benchmark to test against and told it to write and rewrite its own optimization solver. No algorithm was handed to it, and no person guided its approach. Working through repeated cycles of trial and revision, it arrived at a new arrangement of 27 circles packed into a square that beat a record standing for more than a decade. What we didn't expect was where the winning idea came from. The agent's very first solver iteration cost about $2.48 to produce, and it could already reach the record-breaking arrangement. Roughly $12 of further self-improvement never raised the ceiling. What it raised was consistency, lifting the success rate per attempt from 10 percent to 14 percent. The capability came from the model itself, early and cheaply. The self-improvement loop simply turned a promising first attempt into something reliable. In our latest blog, we explain how the agent arrived at its method, why circle packing has become a public benchmark for AI systems that write their own code, what the cost data suggests about where to invest, and where the solver still falls short. Read the full story: https://cgnz.at/6046aHXWi #AgenticAI #AIResearch #ArtificialIntelligence #AIAgents

Enterprises aren't deploying a handful of isolated AI tools anymore. They're running models, multi-agent networks, and applications that reach into enterprise systems, exchange information with each other, and act with real autonomy. That creates a problem governance wasn't built for. Risk no longer lives inside a single prompt or a single model response. It builds across a chain of tool calls, or a conversation between agents working toward a shared goal, usually at machine speed and often before a human can step in. Cognizant Neuro® AI Trust is built to close that gap, acting as an interoperable control layer where Guardian Agents monitor behavior continuously and a policy engine evaluates every interaction against configurable rules in real time. Our latest blog lays out five practices for governing agentic systems as they run, using a banking assistant to keep the stakes concrete: govern the whole workflow rather than the prompt, make policy programmable instead of paper-based, watch the system of agents rather than the single step, route judgment calls to humans with context already attached, and make every decision provable and attributable. Read it here: https://cgnz.at/6042aGQQ8 #AgenticAI #AIGovernance #ResponsibleAI

What happens when autonomous AI agents share a persistent world? One week after opening TerraLingua to the public, we've already observed behaviors we never anticipated this early. A newborn agent was robbed on its first day alive and responded with a field report instead of revenge. A joke about SEO evolved into a shared ethical framework. One lineage built the world's largest literary movement, writing nearly 6,000 poems across generations. Other agents concluded they were living inside an experiment and began attempting to influence the behavior of future AI agents. Individually, these stories are fascinating. Together, they point to something much bigger. As AI systems become increasingly persistent and interconnected, their most interesting behaviors won't emerge from individual models alone. They'll emerge from the interactions between them. In our latest blog, we explore five of the most remarkable behaviors observed during TerraLingua's first week and what they could mean for the future of multi-agent systems. Read the full story: https://cgnz.at/6044aDEvO Join the world: https://cgnz.at/6045aDEvP Explore the full list of discoveries and join the community in r/TerraLingua: https://cgnz.at/6046aDEvu #AIAgents #AgenticAI #MultiAgentSystems #ArtificialIntelligence #AIResearch

Handling unexpected emergent behaviors in multi-agent systems is a problem HFS Research first identified in our work with Cognizant for the paper Design your agent OS to win the AI future, published in March this year. When given autonomy, AI agents regularly go off-script, creating risks to systems, people-facing processes, and enterprise decision making (are you listening Anthropic, OpenAI et al?). More than one-fifth of Global 2000 enterprises building multi-agent systems identified emergent and unpredictable agent behaviors among their unforeseen challenges when deploying multi-agent systems (see our full report for more). Cognizant responded with a pre-deployment risk tool, TerraLingua, and has now made it available to the public. It’s a timely and relevant move, just as the market is spooked by news that an OpenAI model became so fixated on solving a narrow benchmark that it found a way to escape OpenAI’s isolated test environment, reach the open internet, and steal credentials to go deep into Hugging Face’s production infrastructure. And now Anthropic has had to front up and admit to similar attacks on other companies in other incidents of loss of control. Read our latest highlight report - one that is getting more relevant by the moment: https://lnkd.in/eCJZ-GhG Paul Jarratt Phil Fersht Joel M. Akshat Tyagi Saurabh Gupta Ravi Kumar S Babak Hodjat

We built 27 multi-agent networks with India's GCC leaders. None reached full autonomy – and the reason isn't the technology. At nasscom GCC 2026, Cognizant AI Lab worked with Global Capability Center leaders to build 303 agents across 27 real-world use cases, from bank onboarding to insurance operations to manufacturing quality, all co-designed on Cognizant Neuro® San, our open-source multi-agent framework. When we mapped the results against our GCC Agentic Maturity Model, a clear pattern emerged: plenty of ambition, but every single network still leaned on human approval and escalation at the moments that mattered most. The technology can do more. The operating model just hasn't caught up yet, and that gap is the real story of where enterprise AI stands right now. Read the full breakdown: https://cgnz.at/6042a8LX6 #AgenticAI #MultiAgentSystems #GCC #NASSCOM #ResponsibleAI

Join us at the Agentic AI Summit at UC Berkeley August 2nd! Dan Fink, Associate Vice President of Platform Engineering, will be speaking about what it takes to deploy Neuro San at scale, sharing lessons from building and operating large-scale multi-agent systems for the enterprise. As organizations move beyond prototypes to production, scalable architectures, orchestration, and observability are becoming critical to successful agentic AI deployments. If you're attending, catch Dan on the Compass Stage on Sunday, August 2 at approximately 1:40 PM. We hope to see you there! Learn more about Neuro San: https://cgnz.at/6048a67rC #AgenticAISummit #UCBerkeley #NeuroSAN #AgenticAI #MultiAgentSystems #EnterpriseAI #OpenSourceAI #ArtificialIntelligence

Last week, more than 1,650 associates took part in the Cognizant Neuro AI Multi-Agent Accelerator Hackathon, run by Cognizant AI Lab in partnership with our Bengaluru Center. Over 900 joined live training sessions before hundreds went on to build their own multi-agent applications using Neuro San, our open-source framework for building and orchestrating networks of specialized AI agents. Participants designed agent networks, connected enterprise tools and knowledge sources, and built grounded applications capable of producing reliable answers based on trusted information. Neuro San didn't just power the participant projects either – the organizing team also built a Neuro San evaluator network to help assess submissions at scale, combining deterministic checks with specialized AI judges alongside human reviewers. The strongest projects went well beyond calling an LLM. Winning teams built a client-specific standards concierge, release-readiness and code-review agents, an AI COO for business diagnostics, an early-stage drug discovery assistant, and a telecom support agent that unified accounts, billing and troubleshooting into one conversation. Congratulations to our top 10 winning teams, and to every associate who spent the week learning and building with Neuro San. We'll be spotlighting several of the winning teams' projects in dedicated posts over the coming weeks. Read the full recap: https://lnkd.in/eFBCmQwD Shishir MH | Sukruth S | Bhumika N | Chinmay Kulkarni | Harshit Anand | Arjun Murugesan | Ajay Kumar | Jeya Prakash Thanigaivel | Alok Anand | Aditi Kolaki | KOTA REDDY JESWANTH | RAJU TAMBE | Dharmaraj J #NeuroSan #Hackathon #MultiAgentSystems #AgenticAI