Agent Smriti
स्मृति — Sanskrit for “that which is remembered and understood”
The Context Layer for Autonomous AI Agents
Most AI systems don't fail because they lack intelligence. They fail because they lack context. Agent Smriti gives agents persistent, structured awareness — enabling continuity, accuracy, and improvement at scale.
Stateless AI breaks real workflows
Without context, even the most capable AI becomes unreliable in production.
Agents restart from zero
Every interaction begins without knowledge of what came before.
Goals are lost across steps
Multi-step workflows lose intent as agents hand off tasks.
Decisions ignore past actions
No learning from outcomes - the same mistakes recur.
Outputs become inconsistent
Without context, results vary unpredictably across runs.
This leads to one outcome: agents that react, but never understand.
AI needs a new layer: Context
Most AI stacks have models, tools, and workflows.
What's missing: a system that connects everything over time.
A system that connects goals, history, and actions over time
Agents that operate with awareness, not just instructions
Context that compounds with every cycle
Decisions grounded in the full reality of the situation
Agent Smriti introduces the Context Layer —
the foundation that makes agents stateful, aware, and continuously improving.
Not memory. An active reasoning system.
Agent Smriti is a dynamic context engine that continuously builds a live understanding of workflows, prioritizes what matters right now, and injects relevant context into every decision.
This ensures agents don't just respond — they understand and improve.
Context is built from
Four stages. One continuous intelligence loop.
Every agent decision is powered by a live, evolving context — not a static snapshot.
Context Construction
Builds a live context graph - not a static memory store
Aggregates context from user intent, workflow state, APIs, and historical interactions. Creates a dynamic graph that continuously reflects the real state of your workflows.
- User intent and goals
- Workflow state and system data
- Historical interactions and outcomes
Context Prioritization
Agents focus only on what matters right now
Ranks context dynamically using current objective, recency, and task dependencies. Irrelevant context is suppressed - agents act on signal, not noise.
- Ranked by current objective
- Weighted by recency and importance
- Filtered by task dependencies
Context Injection
Right context, at the right moment, for every decision
Injects relevant context into model prompts, decision layers, and action workflows before every agent action. The result: fewer hallucinations, higher accuracy, goal-aligned outputs.
- Injected into model prompts
- Applied across decision layers
- Embedded in action workflows
Context Evolution
Context becomes sharper with every cycle
Updates continuously based on action outcomes, performance signals, and feedback loops. Every run makes context more accurate - agents improve without retraining.
- Outcome-driven context updates
- Performance signal integration
- Continuous feedback loops
A complete agent intelligence system
Agent Smriti doesn't work alone. It powers — and is powered by — the rest of the Nagent platform.
Agent Orchestration
Smriti provides shared context across agents — enabling coordinated, multi-agent execution at scale with unified understanding and seamless handoffs.
KARMIC Feedback Loop
Smriti feeds into KARMIC's evaluation system — every action is evaluated, outcomes update context, and agents improve iteratively.
Together, this is what enables true autonomous systems.
Five layers. One complete picture.
Together, these form a live decision substrate for every agent action.
01
What needs to be achieved
02
Where the workflow stands
03
Rules, data, domain understanding
04
Past actions and decisions
05
What worked, what failed
Smriti in the real world
Autonomous Marketing Execution
Typical outcomes
Intelligent Operations Workflows
Typical outcomes
The context difference
“Before Smriti, our agents were reactive. Now they operate with memory, intent, and continuity. The difference is night and day.”
VP Marketing
Enterprise FMCG Brand
“The biggest shift wasn't automation - it was consistency. Our workflows finally behave like a system, not isolated actions.”
Head of Operations
SaaS Company
From stateless execution → context-aware intelligence
Without Smriti
With Smriti
Reduced errors — from missing context across workflows
Faster execution cycles — through continuous context alignment
Consistent outcomes — at scale, without manual correction
Frequently asked questions
Everything you need to know about Agent Smriti and the context layer.
See Agent Smriti in your workflows
Talk to a Nagent solutions engineer and get a live walkthrough tailored to your use case.