back_office_ops · saas · workflow
Context Engineering for AI Agents: Lessons from Building Manus
Building a production AI agent system required solving interrelated context management challenges: KV-cache efficiency for cost and latency, exploding action spaces from tool proliferation, context window exhaustion on unstructured data, and agent goal drift over long multi-step tasks.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User input received
After receiving a user input, the agent proceeds through a chain of tool uses to complete the task.
Tools used
vLLM
Outcome
Manus arrived at a stable set of context engineering principles enabling the agent loop to ship improvements in hours instead of weeks and operate at scale across millions of users.
What failed first
An early approach of dynamically adding and removing tools mid-iteration using a RAG-like mechanism was abandoned because tool changes invalidated the KV-cache and confused the model when prior actions referenced tools no longer in the context.
Results
Volumearound 100:1
Cost replaced10x difference
Grounding & classification
Source type: technical build writeup
19 fields verified against source quotes, 3 dropped as unverifiable.
agentic workflowai agentknowledge basebuilder submittedfailure mode describedmetric backedproduction runtime claimedvendor confirmedworkflow describedsoftwarecost reductiontime savedtechnical build writeupback office opsagentic task execution